Removing Outliers

## [1] "Outliers : 3qq8dp8jk, 79pn8m6v8, e58u3sinl, hudayxdge, w2x28nknu"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  1"
## [1] "Total number of outliers perceptive task:  1"
## [1] "Total number of outliers logical task:  3"

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2268.1   2290.2  -1130.0   2260.1     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9846 -0.7313  0.2308  0.7546  2.8895 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5178   0.7196  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.1555     0.1548  -7.464 8.42e-14 ***
## difficulty    3.0512     0.2019  15.113  < 2e-16 ***
## timeNorm     -0.3871     0.1728  -2.241   0.0251 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.488       
## timeNorm   -0.430 -0.167
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1881         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-0.9973563  
##  1st Qu.:-0.4243437  
##  Median :-0.1362009  
##  Mean   :-0.0003041  
##  3rd Qu.: 0.3781255  
##  Max.   : 1.6570924  
## [1] "Intercept: -1.16 8.4e-14 ***"
## [1] "Difficulty: 3.05 1.3e-51 ***"
## [1] "Time: -0.387 0.025 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2300"
##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1535.4   1557.4   -763.7   1527.4     1811 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2914 -0.4479  0.1164  0.3982  4.7670 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5772   0.7598  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.2311     0.1826 -12.217  < 2e-16 ***
## difficulty    7.0302     0.3250  21.631  < 2e-16 ***
## timeNorm     -1.0832     0.2369  -4.572 4.84e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.458       
## timeNorm   -0.385 -0.358
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1815 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.527169  
##  1st Qu.:-0.388916  
##  Median :-0.005975  
##  Mean   : 0.002363  
##  3rd Qu.: 0.374680  
##  Max.   : 1.350107  
## [1] "Intercept: -2.23 2.5e-34 ***"
## [1] "Difficulty: 7.03 9.1e-104 ***"
## [1] "Time: -1.08 4.8e-06 ***"
## [1] "R2 fixed: 0.55"
## [1] "R2 mixed: 0.62"
## [1] "Cross Val: 0.81"
## [1] "AIC: 1500"
##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1816.7   1838.8   -904.3   1808.7     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7618 -0.5239 -0.1972  0.5160  5.0573 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.067    1.033   
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.6536     0.1924  -8.596   <2e-16 ***
## difficulty    5.4305     0.2647  20.515   <2e-16 ***
## timeNorm     -2.0774     0.2224  -9.340   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.388       
## timeNorm   -0.276 -0.437
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1881         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.492039  
##  1st Qu.:-0.741161  
##  Median :-0.213560  
##  Mean   : 0.004668  
##  3rd Qu.: 0.599760  
##  Max.   : 2.373359  
## [1] "Intercept: -1.65 8.2e-18 ***"
## [1] "Difficulty: 5.43 1.6e-93 ***"
## [1] "Time: -2.08 9.6e-21 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.53"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1800"
##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0832, p-value = 0.2787
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1121498

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.27984, p-value = 0.7796
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02959975

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18429, p-value = 0.8538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01913758

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.92279, p-value = 0.3561
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09432639

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.40055, p-value = 0.6887
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04164333

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.83074, p-value = 0.4061
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08524489

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.5588, p-value = 0.0105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3482495 
## 
## [1] "self.eff.on.level.s 0.35 0.011 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.77294, p-value = 0.4396
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1034345

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2531, p-value = 0.2102
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1232133

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9255, p-value = 0.05417
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1918732 
## 
## [1] "risk.av.on.level.s 0.19 0.054 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0617, p-value = 0.2884
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1042971

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0129, p-value = 0.3111
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09643322
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0949, p-value = 0.03618
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2036664 
## 
## [1] "age.on.level.s 0.2 0.036 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2495, p-value = 0.2115
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1192254

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.3361, p-value = 0.01949
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.257113 
## 
## [1] "sexe.on.level.m -0.26 0.019 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0, p-value = 1
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau 
##   0

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18884, p-value = 0.8502
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02078441

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 223, p-value = 0.01897
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.85282846 -0.09534056
## sample estimates:
## difference in location 
##             -0.5051082 
## 
## [1] "sexe.on.level.m.2 -0.51 0.019 * mean(A): 0.16 mean(B): -0.32"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 333, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.3670949  0.4731302
## sample estimates:
## difference in location 
##          -0.0009246191

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 340, p-value = 0.8583
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7335260  0.5047401
## sample estimates:
## difference in location 
##            -0.02802612

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.62185, p-value = 0.534
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03720939

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.4464, p-value = 0.0005681
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2033235 
## 
## [1] "pbg.on.error -0.2 0.00057 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.44873, p-value = 0.6536
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02338143

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.23405, p-value = 0.8149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02130326

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.094374, p-value = 0.9248
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008754209

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.45433, p-value = 0.6496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04135338

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 4.1645, p-value = 3.12e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2646112 
## 
## [1] "sexe.on.error 0.26 3.1e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3699, p-value = 0.01779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2608393 
## 
## [1] "sexe.on.error.m 0.26 0.018 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.565, p-value = 0.01032
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2875846 
## 
## [1] "sexe.on.error.s 0.29 0.01 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2318, p-value = 0.02563
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2456339 
## 
## [1] "sexe.on.error.l 0.25 0.026 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4376, p-value = 3.143e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.04977679 0.13237866
## sample estimates:
## difference in location 
##             0.09299933 
## 
## [1] "sexe.on.error.2 0.093 3.1e-05 *** mean(A): -0.093 mean(B): 0.001"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 501, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01355287 0.15331497
## sample estimates:
## difference in location 
##             0.09290042 
## 
## [1] "sexe.on.error.m.2 0.093 0.017 * mean(A): -0.085 mean(B): 0.0073"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 476, p-value = 0.009655
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.02092227 0.15744127
## sample estimates:
## difference in location 
##             0.09796631 
## 
## [1] "sexe.on.error.s.2 0.098 0.0097 ** mean(A): -0.1 mean(B): -0.0014"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 481, p-value = 0.02523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.009389481 0.150561466
## sample estimates:
## difference in location 
##             0.09060751 
## 
## [1] "sexe.on.error.l.2 0.091 0.025 * mean(A): -0.091 mean(B): -0.0033"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.60676, p-value = 0.544
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03431688

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.12035, p-value = 0.9042
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01183404

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.11152, p-value = 0.9112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01111235

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.79275, p-value = 0.4279
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07787518

Self efficacy and subjective difficulty error

## Warning: Removed 84 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.9644, p-value = 0.003033
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2277125 
## 
## [1] "self.eff.on.error -0.23 0.003 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7653, p-value = 0.07751
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2402652 
## 
## [1] "self.eff.on.error -0.24 0.078 ."
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2240675 
## 
## [1] "self.eff.on.error -0.22 0.1 :("
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6401, p-value = 0.101
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2194829

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0096 47     0.64 :(
##  2:      0.09375         0.0440 54     0.052 .
##  3:      0.15625         0.0045 58     0.91 :(
##  4:      0.21875         0.0260 58     0.27 :(
##  5:      0.28125         0.0044 57     0.98 :(
##  6:      0.34375        -0.0400 58     0.25 :(
##  7:      0.40625        -0.0400 58     0.23 :(
##  8:      0.46875        -0.0045 58     0.94 :(
##  9:      0.53125        -0.0190 58     0.54 :(
## 10:      0.59375        -0.0420 58     0.18 :(
## 11:      0.65625        -0.0370 58     0.31 :(
## 12:      0.71875        -0.1100 58 1.9e-05 ***
## 13:      0.78125        -0.1400 58 7.4e-08 ***
## 14:      0.84375        -0.2100 58 1.7e-09 ***
## 15:      0.90625        -0.1900 57   5e-11 ***
## 16:      0.96875        -0.1800 55 1.1e-10 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 47     0.64 :(
##  2: 54     0.052 .
##  3: 58     0.91 :(
##  4: 58     0.27 :(
##  5: 57     0.98 :(
##  6: 58     0.25 :(
##  7: 58     0.23 :(
##  8: 58     0.94 :(
##  9: 58     0.54 :(
## 10: 58     0.18 :(
## 11: 58     0.31 :(
## 12: 58 1.9e-05 ***
## 13: 58 7.4e-08 ***
## 14: 58 1.7e-09 ***
## 15: 57   5e-11 ***
## 16: 55 1.1e-10 ***
## [1] 56.8
## [1] 2.86

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0240 32     0.15 :(
##  2:      0.09375         0.0390 34     0.19 :(
##  3:      0.15625        -0.0310 42     0.35 :(
##  4:      0.21875        -0.0045 40     0.79 :(
##  5:      0.28125        -0.0190 38      0.6 :(
##  6:      0.34375        -0.0290 37     0.72 :(
##  7:      0.40625         0.0100 36     0.87 :(
##  8:      0.46875         0.0670 38      0.1 :(
##  9:      0.53125         0.0640 40     0.22 :(
## 10:      0.59375        -0.0220 39     0.81 :(
## 11:      0.65625        -0.0130 35     0.76 :(
## 12:      0.71875        -0.1400 37   0.0024 **
## 13:      0.78125        -0.1300 37   0.0039 **
## 14:      0.84375        -0.2200 29 4.2e-05 ***
## 15:      0.90625        -0.1900 22 4.1e-05 ***
## 16:      0.96875        -0.1500 11   0.0035 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.15 :(
##  2: 34     0.19 :(
##  3: 42     0.35 :(
##  4: 40     0.79 :(
##  5: 38      0.6 :(
##  6: 37     0.72 :(
##  7: 36     0.87 :(
##  8: 38      0.1 :(
##  9: 40     0.22 :(
## 10: 39     0.81 :(
## 11: 35     0.76 :(
## 12: 37   0.0024 **
## 13: 37   0.0039 **
## 14: 29 4.2e-05 ***
## 15: 22 4.1e-05 ***
## 16: 11   0.0035 **
## [1] 34.2
## [1] 7.86

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 24     0.015 *
##  2:      0.09375         0.0190 33     0.47 :(
##  3:      0.15625        -0.0660 39     0.13 :(
##  4:      0.21875        -0.0045 43     0.52 :(
##  5:      0.28125        -0.0310 43     0.66 :(
##  6:      0.34375        -0.0940 37     0.041 *
##  7:      0.40625        -0.1200 44     0.069 .
##  8:      0.46875        -0.0710 42     0.11 :(
##  9:      0.53125        -0.0810 41     0.19 :(
## 10:      0.59375        -0.0940 38     0.089 .
## 11:      0.65625        -0.0130 42     0.68 :(
## 12:      0.71875        -0.0960 41     0.028 *
## 13:      0.78125        -0.1400 43 0.00017 ***
## 14:      0.84375        -0.1900 42   6e-06 ***
## 15:      0.90625        -0.2000 39 5.3e-08 ***
## 16:      0.96875        -0.2000 37 1.1e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 24     0.015 *
##  2: 33     0.47 :(
##  3: 39     0.13 :(
##  4: 43     0.52 :(
##  5: 43     0.66 :(
##  6: 37     0.041 *
##  7: 44     0.069 .
##  8: 42     0.11 :(
##  9: 41     0.19 :(
## 10: 38     0.089 .
## 11: 42     0.68 :(
## 12: 41     0.028 *
## 13: 43 0.00017 ***
## 14: 42   6e-06 ***
## 15: 39 5.3e-08 ***
## 16: 37 1.1e-07 ***
## [1] 39.2
## [1] 5.01

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  3          NA
##  2:      0.09375          0.049 12     0.72 :(
##  3:      0.15625          0.022 19     0.48 :(
##  4:      0.21875          0.021 20     0.69 :(
##  5:      0.28125          0.040 20     0.51 :(
##  6:      0.34375          0.085 21     0.22 :(
##  7:      0.40625         -0.013 21     0.86 :(
##  8:      0.46875         -0.040 19     0.48 :(
##  9:      0.53125         -0.100 17     0.13 :(
## 10:      0.59375         -0.076 22     0.27 :(
## 11:      0.65625         -0.085 21     0.25 :(
## 12:      0.71875         -0.150 24   0.0037 **
## 13:      0.78125         -0.100 24     0.017 *
## 14:      0.84375         -0.180 25 0.00068 ***
## 15:      0.90625         -0.120 25 1.2e-05 ***
## 16:      0.96875         -0.180 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 12     0.72 :(
##  2: 19     0.48 :(
##  3: 20     0.69 :(
##  4: 20     0.51 :(
##  5: 21     0.22 :(
##  6: 21     0.86 :(
##  7: 19     0.48 :(
##  8: 17     0.13 :(
##  9: 22     0.27 :(
## 10: 21     0.25 :(
## 11: 24   0.0037 **
## 12: 24     0.017 *
## 13: 25 0.00068 ***
## 14: 25 1.2e-05 ***
## 15: 25 1.3e-05 ***
## [1] 21
## [1] 3.53
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0940  8   0.71 :(
##  3:      0.15625        -0.0990 29   0.021 *
##  4:      0.21875        -0.0760 41   0.042 *
##  5:      0.28125        -0.0540 48    0.2 :(
##  6:      0.34375        -0.0400 50   0.22 :(
##  7:      0.40625        -0.0015 50    0.9 :(
##  8:      0.46875        -0.0022 54      1 :(
##  9:      0.53125         0.0400 52   0.17 :(
## 10:      0.59375         0.0063 51   0.82 :(
## 11:      0.65625         0.0220 52   0.79 :(
## 12:      0.71875        -0.0580 53   0.064 .
## 13:      0.78125        -0.0790 46   0.015 *
## 14:      0.84375        -0.0940 29   0.077 .
## 15:      0.90625        -0.0760 13 0.0012 **
## 16:      0.96875        -0.1100  6   0.031 *
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.71 :(
##  2: 29   0.021 *
##  3: 41   0.042 *
##  4: 48    0.2 :(
##  5: 50   0.22 :(
##  6: 50    0.9 :(
##  7: 54      1 :(
##  8: 52   0.17 :(
##  9: 51   0.82 :(
## 10: 52   0.79 :(
## 11: 53   0.064 .
## 12: 46   0.015 *
## 13: 29   0.077 .
## 14: 13 0.0012 **
## 15:  6   0.031 *
## [1] 38.8
## [1] 17.3
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375         -0.094  8 0.71 :(
##  3:      0.15625         -0.099 24 0.023 *
##  4:      0.21875         -0.066 25 0.067 .
##  5:      0.28125         -0.043 25 0.31 :(
##  6:      0.34375         -0.040 25 0.32 :(
##  7:      0.40625          0.040 24  0.4 :(
##  8:      0.46875          0.067 24 0.12 :(
##  9:      0.53125          0.110 23 0.021 *
## 10:      0.59375          0.120 22 0.043 *
## 11:      0.65625          0.029 22 0.52 :(
## 12:      0.71875         -0.040 21 0.094 .
## 13:      0.78125         -0.067 15 0.32 :(
## 14:      0.84375             NA  2      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  8 0.71 :(
##  2: 24 0.023 *
##  3: 25 0.067 .
##  4: 25 0.31 :(
##  5: 25 0.32 :(
##  6: 24  0.4 :(
##  7: 24 0.12 :(
##  8: 23 0.021 *
##  9: 22 0.043 *
## 10: 22 0.52 :(
## 11: 21 0.094 .
## 12: 15 0.32 :(
## [1] 21.5
## [1] 5.07
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  5      NA
##  4:      0.21875        -0.0045 16 0.41 :(
##  5:      0.28125        -0.0670 23 0.51 :(
##  6:      0.34375        -0.0580 24  0.3 :(
##  7:      0.40625        -0.0320 25 0.73 :(
##  8:      0.46875        -0.0400 25  0.5 :(
##  9:      0.53125         0.0220 25 0.69 :(
## 10:      0.59375        -0.0220 22  0.9 :(
## 11:      0.65625         0.0410 23 0.66 :(
## 12:      0.71875         0.0310 25 0.65 :(
## 13:      0.78125        -0.0670 25 0.13 :(
## 14:      0.84375        -0.0940 20 0.15 :(
## 15:      0.90625             NA  6      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 16 0.41 :(
##  2: 23 0.51 :(
##  3: 24  0.3 :(
##  4: 25 0.73 :(
##  5: 25  0.5 :(
##  6: 25 0.69 :(
##  7: 22  0.9 :(
##  8: 23 0.66 :(
##  9: 25 0.65 :(
## 10: 25 0.13 :(
## 11: 20 0.15 :(
## [1] 23
## [1] 2.83
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         -0.150 5 0.28 :(
##  9:      0.53125         -0.220 4 0.38 :(
## 10:      0.59375         -0.290 7 0.078 .
## 11:      0.65625         -0.130 7 0.35 :(
## 12:      0.71875         -0.260 7 0.047 *
## 13:      0.78125         -0.160 6 0.16 :(
## 14:      0.84375         -0.120 7  0.2 :(
## 15:      0.90625         -0.081 7 0.022 *
## 16:      0.96875         -0.110 6 0.031 *
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  5 0.28 :(
## 2:  4 0.38 :(
## 3:  7 0.078 .
## 4:  7 0.35 :(
## 5:  7 0.047 *
## 6:  6 0.16 :(
## 7:  7  0.2 :(
## 8:  7 0.022 *
## 9:  6 0.031 *
## [1] 6.22
## [1] 1.09
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 32     0.034 *
##  2:      0.09375        -0.0065 48     0.64 :(
##  3:      0.15625        -0.0970 51   0.0069 **
##  4:      0.21875        -0.0760 47   0.0011 **
##  5:      0.28125        -0.0670 46      0.1 :(
##  6:      0.34375        -0.1300 41     0.063 .
##  7:      0.40625        -0.1200 44     0.053 .
##  8:      0.46875        -0.1100 42     0.036 *
##  9:      0.53125        -0.1700 34   0.0079 **
## 10:      0.59375        -0.2400 37 0.00062 ***
## 11:      0.65625        -0.1100 40     0.12 :(
## 12:      0.71875        -0.1700 46 0.00063 ***
## 13:      0.78125        -0.1700 42   0.0042 **
## 14:      0.84375        -0.1700 46   9e-06 ***
## 15:      0.90625        -0.1600 53 1.9e-10 ***
## 16:      0.96875        -0.1400 55 9.4e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.034 *
##  2: 48     0.64 :(
##  3: 51   0.0069 **
##  4: 47   0.0011 **
##  5: 46      0.1 :(
##  6: 41     0.063 .
##  7: 44     0.053 .
##  8: 42     0.036 *
##  9: 34   0.0079 **
## 10: 37 0.00062 ***
## 11: 40     0.12 :(
## 12: 46 0.00063 ***
## 13: 42   0.0042 **
## 14: 46   9e-06 ***
## 15: 53 1.9e-10 ***
## 16: 55 9.4e-11 ***
## [1] 44
## [1] 6.4

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.054 10   0.36 :(
##  2:      0.09375             NA 10        NA
##  3:      0.15625         -0.110  9   0.18 :(
##  4:      0.21875         -0.150  5   0.099 .
##  5:      0.28125         -0.100  8   0.53 :(
##  6:      0.34375         -0.130  6    0.4 :(
##  7:      0.40625         -0.190  7   0.27 :(
##  8:      0.46875         -0.250  9    0.07 .
##  9:      0.53125         -0.210  7    0.2 :(
## 10:      0.59375         -0.380  6   0.058 .
## 11:      0.65625         -0.085  5   0.59 :(
## 12:      0.71875         -0.290  9   0.044 *
## 13:      0.78125         -0.250  7   0.15 :(
## 14:      0.84375         -0.130  7    0.2 :(
## 15:      0.90625         -0.085  9  0.008 **
## 16:      0.96875         -0.150 10 0.0053 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 10   0.36 :(
##  2:  9   0.18 :(
##  3:  5   0.099 .
##  4:  8   0.53 :(
##  5:  6    0.4 :(
##  6:  7   0.27 :(
##  7:  9    0.07 .
##  8:  7    0.2 :(
##  9:  6   0.058 .
## 10:  5   0.59 :(
## 11:  9   0.044 *
## 12:  7   0.15 :(
## 13:  7    0.2 :(
## 14:  9  0.008 **
## 15: 10 0.0053 **
## [1] 7.6
## [1] 1.68
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 19    0.001 **
##  2:      0.09375         -0.004 27     0.97 :(
##  3:      0.15625         -0.160 26 9.3e-05 ***
##  4:      0.21875         -0.150 24 0.00077 ***
##  5:      0.28125         -0.170 22      0.05 .
##  6:      0.34375         -0.170 19     0.14 :(
##  7:      0.40625         -0.190 22     0.025 *
##  8:      0.46875         -0.040 23     0.45 :(
##  9:      0.53125         -0.220 18      0.02 *
## 10:      0.59375         -0.270 20   0.0015 **
## 11:      0.65625         -0.160 22     0.079 .
## 12:      0.71875         -0.150 21   0.0059 **
## 13:      0.78125         -0.250 19     0.034 *
## 14:      0.84375         -0.220 24 0.00023 ***
## 15:      0.90625         -0.190 27 5.7e-06 ***
## 16:      0.96875         -0.150 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19    0.001 **
##  2: 27     0.97 :(
##  3: 26 9.3e-05 ***
##  4: 24 0.00077 ***
##  5: 22      0.05 .
##  6: 19     0.14 :(
##  7: 22     0.025 *
##  8: 23     0.45 :(
##  9: 18      0.02 *
## 10: 20   0.0015 **
## 11: 22     0.079 .
## 12: 21   0.0059 **
## 13: 19     0.034 *
## 14: 24 0.00023 ***
## 15: 27 5.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 22.5
## [1] 3.1

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  3          NA
##  2:      0.09375         0.0650 11     0.56 :(
##  3:      0.15625         0.0360 16     0.42 :(
##  4:      0.21875        -0.0045 18     0.76 :(
##  5:      0.28125         0.0045 16      0.9 :(
##  6:      0.34375        -0.1300 16     0.58 :(
##  7:      0.40625         0.0580 15     0.51 :(
##  8:      0.46875        -0.1000 10     0.15 :(
##  9:      0.53125        -0.1000  9     0.72 :(
## 10:      0.59375        -0.1500 11     0.82 :(
## 11:      0.65625         0.0220 13     0.78 :(
## 12:      0.71875        -0.0400 16     0.36 :(
## 13:      0.78125        -0.0710 16     0.26 :(
## 14:      0.84375        -0.0820 15      0.07 .
## 15:      0.90625        -0.1600 17 0.00027 ***
## 16:      0.96875        -0.1300 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.56 :(
##  2: 16     0.42 :(
##  3: 18     0.76 :(
##  4: 16      0.9 :(
##  5: 16     0.58 :(
##  6: 15     0.51 :(
##  7: 10     0.15 :(
##  8:  9     0.72 :(
##  9: 11     0.82 :(
## 10: 13     0.78 :(
## 11: 16     0.36 :(
## 12: 16     0.26 :(
## 13: 15      0.07 .
## 14: 17 0.00027 ***
## 15: 18 0.00021 ***
## [1] 14.5
## [1] 2.92
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0005 38     0.78 :(
##  2:      0.09375         0.0970 43      0.01 *
##  3:      0.15625         0.0940 48      0.04 *
##  4:      0.21875         0.1600 50   0.0046 **
##  5:      0.28125         0.1500 49     0.015 *
##  6:      0.34375         0.0850 41      0.08 .
##  7:      0.40625         0.0220 47     0.77 :(
##  8:      0.46875        -0.0400 47     0.64 :(
##  9:      0.53125         0.0160 45     0.73 :(
## 10:      0.59375        -0.0370 46      0.6 :(
## 11:      0.65625        -0.0490 42     0.32 :(
## 12:      0.71875        -0.1500 41 0.00057 ***
## 13:      0.78125        -0.1400 53 0.00026 ***
## 14:      0.84375        -0.2600 52 1.4e-08 ***
## 15:      0.90625        -0.2400 42 1.7e-08 ***
## 16:      0.96875        -0.3300 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.78 :(
##  2: 43      0.01 *
##  3: 48      0.04 *
##  4: 50   0.0046 **
##  5: 49     0.015 *
##  6: 41      0.08 .
##  7: 47     0.77 :(
##  8: 47     0.64 :(
##  9: 45     0.73 :(
## 10: 46      0.6 :(
## 11: 42     0.32 :(
## 12: 41 0.00057 ***
## 13: 53 0.00026 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.7e-08 ***
## 16: 29 2.7e-06 ***
## [1] 44.6
## [1] 5.93

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.012 29     0.55 :(
##  2:      0.09375          0.080 29     0.016 *
##  3:      0.15625          0.068 28     0.25 :(
##  4:      0.21875          0.160 26     0.042 *
##  5:      0.28125          0.076 24     0.28 :(
##  6:      0.34375          0.013 21     0.78 :(
##  7:      0.40625          0.022 21     0.58 :(
##  8:      0.46875          0.100 25     0.39 :(
##  9:      0.53125          0.040 23     0.61 :(
## 10:      0.59375         -0.099 23     0.13 :(
## 11:      0.65625         -0.085 22     0.17 :(
## 12:      0.71875         -0.150 20   0.0092 **
## 13:      0.78125         -0.091 27     0.084 .
## 14:      0.84375         -0.270 25 7.5e-05 ***
## 15:      0.90625         -0.260 15 0.00071 ***
## 16:      0.96875         -0.330  2      0.5 :(
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 29     0.55 :(
##  2: 29     0.016 *
##  3: 28     0.25 :(
##  4: 26     0.042 *
##  5: 24     0.28 :(
##  6: 21     0.78 :(
##  7: 21     0.58 :(
##  8: 25     0.39 :(
##  9: 23     0.61 :(
## 10: 23     0.13 :(
## 11: 22     0.17 :(
## 12: 20   0.0092 **
## 13: 27     0.084 .
## 14: 25 7.5e-05 ***
## 15: 15 0.00071 ***
## 16:  2      0.5 :(
## [1] 22.5
## [1] 6.58

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031  9     0.53 :(
##  2:      0.09375          0.140 13      0.09 .
##  3:      0.15625          0.180 16     0.046 *
##  4:      0.21875          0.140 18     0.13 :(
##  5:      0.28125          0.150 17     0.079 .
##  6:      0.34375          0.085 14     0.31 :(
##  7:      0.40625          0.022 16     0.94 :(
##  8:      0.46875         -0.180 14     0.065 .
##  9:      0.53125          0.040 14     0.34 :(
## 10:      0.59375          0.085 14      0.9 :(
## 11:      0.65625         -0.013 13     0.89 :(
## 12:      0.71875         -0.150 15     0.24 :(
## 13:      0.78125         -0.220 17   0.0024 **
## 14:      0.84375         -0.220 17 0.00091 ***
## 15:      0.90625         -0.230 17 0.00032 ***
## 16:      0.96875         -0.330 17 0.00031 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.53 :(
##  2: 13      0.09 .
##  3: 16     0.046 *
##  4: 18     0.13 :(
##  5: 17     0.079 .
##  6: 14     0.31 :(
##  7: 16     0.94 :(
##  8: 14     0.065 .
##  9: 14     0.34 :(
## 10: 14      0.9 :(
## 11: 13     0.89 :(
## 12: 15     0.24 :(
## 13: 17   0.0024 **
## 14: 17 0.00091 ***
## 15: 17 0.00032 ***
## 16: 17 0.00031 ***
## [1] 15.1
## [1] 2.29

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625             NA  4        NA
##  4:      0.21875          0.210  6   0.21 :(
##  5:      0.28125          0.190  8   0.18 :(
##  6:      0.34375          0.410  6    0.09 .
##  7:      0.40625         -0.031 10   0.92 :(
##  8:      0.46875          0.066  8   0.73 :(
##  9:      0.53125         -0.120  8   0.23 :(
## 10:      0.59375          0.120  9   0.63 :(
## 11:      0.65625          0.130  7   0.93 :(
## 12:      0.71875         -0.280  6   0.031 *
## 13:      0.78125         -0.079  9   0.28 :(
## 14:      0.84375         -0.270 10   0.032 *
## 15:      0.90625         -0.190 10 0.0058 **
## 16:      0.96875         -0.340 10  0.002 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  6   0.21 :(
##  2:  8   0.18 :(
##  3:  6    0.09 .
##  4: 10   0.92 :(
##  5:  8   0.73 :(
##  6:  8   0.23 :(
##  7:  9   0.63 :(
##  8:  7   0.93 :(
##  9:  6   0.031 *
## 10:  9   0.28 :(
## 11: 10   0.032 *
## 12: 10 0.0058 **
## 13: 10  0.002 **
## [1] 8.23
## [1] 1.59
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85425  -0.20543   0.02783   0.20243   0.70750  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006664   0.018685  -0.357 0.721400    
## timeNorm     0.016339   0.020930   0.781 0.435103    
## obj.diff    -0.094710   0.028659  -3.305 0.000969 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07620175)
## 
##     Null deviance: 143.99  on 1880  degrees of freedom
## Residual deviance: 143.11  on 1878  degrees of freedom
## AIC: 500.65
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78155  -0.13780  -0.01151   0.12280   0.83005  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006795   0.014205  -0.478    0.632    
## timeNorm     0.011968   0.020729   0.577    0.564    
## obj.diff    -0.205459   0.018132 -11.331   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0706054)
## 
##     Null deviance: 137.08  on 1814  degrees of freedom
## Residual deviance: 127.94  on 1812  degrees of freedom
## AIC: 344.82
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71153  -0.24157   0.00719   0.24129   0.65825  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17431    0.01800   9.684   <2e-16 ***
## timeNorm     0.02107    0.02417   0.872    0.384    
## obj.diff    -0.46661    0.02329 -20.037   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1007759)
## 
##     Null deviance: 230.47  on 1880  degrees of freedom
## Residual deviance: 189.26  on 1878  degrees of freedom
## AIC: 1026.4
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.3918129     0.4457102 -0.05543814 342 0.0017 **
##  2:      4.5      0.4954052     0.5624859 -0.05783222 171 0.0052 **
##  3:      7.5      0.4928989     0.5357049 -0.03693287 171   0.077 .
##  4:     10.5      0.5071011     0.5362058 -0.02578583 171   0.23 :(
##  5:     13.5      0.4519632     0.5133937 -0.05576376 171 0.0061 **
##  6:     16.5      0.4995823     0.5320036 -0.01539779 171   0.46 :(
##  7:     19.5      0.4803676     0.5358363 -0.04608428 171   0.025 *
##  8:     22.5      0.4527987     0.4961373 -0.03638516 171   0.091 .
##  9:     25.5      0.4536341     0.4868060 -0.02527202 171   0.27 :(
## 10:     28.5      0.4243943     0.4657574 -0.03934980 171   0.074 .
##     time  error.diff shapes
##  1:  1.5 -0.05543814     24
##  2:  4.5 -0.05783222     24
##  3:  7.5 -0.03693287     16
##  4: 10.5 -0.02578583     16
##  5: 13.5 -0.05576376     24
##  6: 16.5 -0.01539779     16
##  7: 19.5 -0.04608428     24
##  8: 22.5 -0.03638516     16
##  9: 25.5 -0.02527202     16
## 10: 28.5 -0.03934980     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2761905     0.3121174 -0.08025653 330 2.2e-05 ***
##  2:      4.5      0.5194805     0.6623901 -0.12246157 165 6.5e-13 ***
##  3:      7.5      0.4259740     0.5756691 -0.12748326 165 6.7e-13 ***
##  4:     10.5      0.4658009     0.6169890 -0.12563962 165 5.6e-14 ***
##  5:     13.5      0.4251082     0.5882784 -0.13475627 165 4.2e-16 ***
##  6:     16.5      0.4025974     0.5480044 -0.12850300 165 1.9e-12 ***
##  7:     19.5      0.4666667     0.5706900 -0.09391859 165 2.3e-08 ***
##  8:     22.5      0.4311688     0.5568448 -0.12173493 165 1.6e-10 ***
##  9:     25.5      0.4891775     0.5635905 -0.08515151 165 7.1e-08 ***
## 10:     28.5      0.4649351     0.5525507 -0.08873994 165 1.2e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.08025653     24
##  2:  4.5 -0.12246157     24
##  3:  7.5 -0.12748326     24
##  4: 10.5 -0.12563962     24
##  5: 13.5 -0.13475627     24
##  6: 16.5 -0.12850300     24
##  7: 19.5 -0.09391859     24
##  8: 22.5 -0.12173493     24
##  9: 25.5 -0.08515151     24
## 10: 28.5 -0.08873994     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.3483709     0.3515160 -0.0257788254 342     0.26 :(
##  2:      4.5      0.5037594     0.6513076 -0.1432600132 171 4.6e-08 ***
##  3:      7.5      0.5037594     0.5682979 -0.0702473202 171   0.0057 **
##  4:     10.5      0.4970760     0.5388474 -0.0530333212 171      0.04 *
##  5:     13.5      0.4761905     0.5225795 -0.0457087630 171     0.099 .
##  6:     16.5      0.4820384     0.5042410 -0.0325739632 171     0.21 :(
##  7:     19.5      0.4185464     0.4415088 -0.0319575055 171     0.25 :(
##  8:     22.5      0.3918129     0.4078173 -0.0213488721 171     0.43 :(
##  9:     25.5      0.3851295     0.3856125 -0.0035008941 171      0.9 :(
## 10:     28.5      0.3792815     0.3513216 -0.0006985616 171     0.98 :(
##     time    error.diff shapes
##  1:  1.5 -0.0257788254     16
##  2:  4.5 -0.1432600132     24
##  3:  7.5 -0.0702473202     24
##  4: 10.5 -0.0530333212     24
##  5: 13.5 -0.0457087630     16
##  6: 16.5 -0.0325739632     16
##  7: 19.5 -0.0319575055     16
##  8: 22.5 -0.0213488721     16
##  9: 25.5 -0.0035008941     16
## 10: 28.5 -0.0006985616     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7569  -0.2283   0.1044   0.1775   0.6607  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12790    0.02644   4.837  1.5e-06 ***
## timeNorm     0.03074    0.03011   1.021    0.307    
## obj.diff    -0.37938    0.03148 -12.051  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0966408)
## 
##     Null deviance: 125.39  on 1154  degrees of freedom
## Residual deviance: 111.33  on 1152  degrees of freedom
## AIC: 583.79
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78332  -0.20655   0.01988   0.20932   0.76046  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04636    0.01554   2.982  0.00289 ** 
## timeNorm     0.03107    0.02013   1.544  0.12281    
## obj.diff    -0.26193    0.02129 -12.305  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08617747)
## 
##     Null deviance: 211.91  on 2309  degrees of freedom
## Residual deviance: 198.81  on 2307  degrees of freedom
## AIC: 897.88
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73041  -0.19642  -0.06578   0.20222   0.76122  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06693    0.01436   4.660 3.36e-06 ***
## timeNorm     0.02111    0.01996   1.058     0.29    
## obj.diff    -0.25660    0.02270 -11.303  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07766279)
## 
##     Null deviance: 173.88  on 2111  degrees of freedom
## Residual deviance: 163.79  on 2109  degrees of freedom
## AIC: 601.63
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4503401     0.5078590 -0.06911867 210     0.011 *
##  2:      4.5      0.6217687     0.7854497 -0.12506562 105   4e-08 ***
##  3:      7.5      0.6231293     0.7571408 -0.11442931 105 2.3e-06 ***
##  4:     10.5      0.5986395     0.7378391 -0.11609559 105 5.6e-06 ***
##  5:     13.5      0.5768707     0.7194038 -0.11978956 105 1.1e-06 ***
##  6:     16.5      0.5496599     0.6928809 -0.11831475 105 7.5e-06 ***
##  7:     19.5      0.5496599     0.6519999 -0.08328775 105   0.0045 **
##  8:     22.5      0.5469388     0.6672242 -0.10170834 105 9.6e-05 ***
##  9:     25.5      0.5306122     0.6374488 -0.09280897 105 0.00011 ***
## 10:     28.5      0.5986395     0.6321308 -0.04493309 105     0.046 *
##     time  error.diff shapes
##  1:  1.5 -0.06911867     24
##  2:  4.5 -0.12506562     24
##  3:  7.5 -0.11442931     24
##  4: 10.5 -0.11609559     24
##  5: 13.5 -0.11978956     24
##  6: 16.5 -0.11831475     24
##  7: 19.5 -0.08328775     24
##  8: 22.5 -0.10170834     24
##  9: 25.5 -0.09280897     24
## 10: 28.5 -0.04493309     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3363946     0.3833571 -0.06472476 420 0.00088 ***
##  2:      4.5      0.5156463     0.6644303 -0.13652906 210 7.9e-13 ***
##  3:      7.5      0.4646259     0.5546648 -0.08926828 210 2.4e-06 ***
##  4:     10.5      0.5142857     0.5856428 -0.08276584 210   4e-05 ***
##  5:     13.5      0.4680272     0.5711545 -0.09916733 210 1.5e-06 ***
##  6:     16.5      0.4884354     0.5645710 -0.08021078 210 0.00025 ***
##  7:     19.5      0.4925170     0.5763408 -0.07570676 210 4.2e-05 ***
##  8:     22.5      0.4278912     0.5129160 -0.09031318 210 2.9e-05 ***
##  9:     25.5      0.4829932     0.5222933 -0.04941546 210     0.038 *
## 10:     28.5      0.4408163     0.5011792 -0.06649074 210   0.0011 **
##     time  error.diff shapes
##  1:  1.5 -0.06472476     24
##  2:  4.5 -0.13652906     24
##  3:  7.5 -0.08926828     24
##  4: 10.5 -0.08276584     24
##  5: 13.5 -0.09916733     24
##  6: 16.5 -0.08021078     24
##  7: 19.5 -0.07570676     24
##  8: 22.5 -0.09031318     24
##  9: 25.5 -0.04941546     24
## 10: 28.5 -0.06649074     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.2823661     0.2812232 -0.034155981 384   0.054 .
##  2:      4.5      0.4322917     0.4940130 -0.065713509 192 0.0054 **
##  3:      7.5      0.4047619     0.4572421 -0.053881101 192   0.016 *
##  4:     10.5      0.4047619     0.4436417 -0.041433663 192   0.054 .
##  5:     13.5      0.3645833     0.4100912 -0.052717074 192   0.014 *
##  6:     16.5      0.3854167     0.3974278 -0.017335375 192   0.39 :(
##  7:     19.5      0.3623512     0.3739496 -0.026088074 192   0.22 :(
##  8:     22.5      0.3556548     0.3577330 -0.012479126 192   0.56 :(
##  9:     25.5      0.3489583     0.3414701 -0.002661268 192   0.91 :(
## 10:     28.5      0.3058036     0.3086979 -0.022918440 192   0.21 :(
##     time   error.diff shapes
##  1:  1.5 -0.034155981     16
##  2:  4.5 -0.065713509     24
##  3:  7.5 -0.053881101     24
##  4: 10.5 -0.041433663     16
##  5: 13.5 -0.052717074     24
##  6: 16.5 -0.017335375     16
##  7: 19.5 -0.026088074     16
##  8: 22.5 -0.012479126     16
##  9: 25.5 -0.002661268     16
## 10: 28.5 -0.022918440     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78307  -0.20030   0.09965   0.20287   0.56643  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.22999    0.11949  -1.925   0.0555 .
## timeNorm    -0.03598    0.06998  -0.514   0.6077  
## obj.diff     0.08283    0.15050   0.550   0.5826  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1038657)
## 
##     Null deviance: 23.736  on 230  degrees of freedom
## Residual deviance: 23.681  on 228  degrees of freedom
## AIC: 137.39
## 
## Number of Fisher Scoring iterations: 2
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5374150     0.7170177 -0.17337940 42     0.011 *
##  2:      4.5      0.6122449     0.7946114 -0.14180958 21   0.0063 **
##  3:      7.5      0.6326531     0.7649789 -0.07966557 21     0.038 *
##  4:     10.5      0.6394558     0.7869246 -0.09982316 21    0.009 **
##  5:     13.5      0.6190476     0.8120284 -0.09702938 21     0.013 *
##  6:     16.5      0.5102041     0.7887369 -0.26023913 21   0.0049 **
##  7:     19.5      0.5442177     0.7250289 -0.16961596 21      0.05 .
##  8:     22.5      0.6462585     0.7637626 -0.03896849 21     0.49 :(
##  9:     25.5      0.5578231     0.8157609 -0.26561302 21 0.00072 ***
## 10:     28.5      0.5986395     0.7674702 -0.09317669 21      0.06 .
##     time  error.diff shapes
##  1:  1.5 -0.17337940     24
##  2:  4.5 -0.14180958     24
##  3:  7.5 -0.07966557     24
##  4: 10.5 -0.09982316     24
##  5: 13.5 -0.09702938     24
##  6: 16.5 -0.26023913     24
##  7: 19.5 -0.16961596     16
##  8: 22.5 -0.03896849     16
##  9: 25.5 -0.26561302     24
## 10: 28.5 -0.09317669     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79768  -0.22927   0.04496   0.19205   0.68904  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04646    0.03146  -1.477    0.140
## timeNorm     0.01656    0.03145   0.527    0.599
## obj.diff    -0.01154    0.04892  -0.236    0.814
## 
## (Dispersion parameter for gaussian family taken to be 0.07542452)
## 
##     Null deviance: 62.024  on 824  degrees of freedom
## Residual deviance: 61.999  on 822  degrees of freedom
## AIC: 213.93
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.4219048     0.4703926 -0.04882302 150 0.077 .
##  2:      4.5      0.5314286     0.6181213 -0.07545117  75  0.02 *
##  3:      7.5      0.5028571     0.5405554 -0.02915190  75 0.35 :(
##  4:     10.5      0.5333333     0.5682867 -0.03243828  75 0.34 :(
##  5:     13.5      0.5200000     0.5516441 -0.02674953  75 0.43 :(
##  6:     16.5      0.5428571     0.5685882 -0.02122707  75 0.62 :(
##  7:     19.5      0.5447619     0.5794923 -0.02965771  75 0.39 :(
##  8:     22.5      0.4380952     0.5231952 -0.09195474  75 0.015 *
##  9:     25.5      0.4819048     0.5079792 -0.03043348  75 0.41 :(
## 10:     28.5      0.4819048     0.5148979 -0.03878182  75 0.31 :(
##     time  error.diff shapes
##  1:  1.5 -0.04882302     16
##  2:  4.5 -0.07545117     24
##  3:  7.5 -0.02915190     16
##  4: 10.5 -0.03243828     16
##  5: 13.5 -0.02674953     16
##  6: 16.5 -0.02122707     16
##  7: 19.5 -0.02965771     16
##  8: 22.5 -0.09195474     24
##  9: 25.5 -0.03043348     16
## 10: 28.5 -0.03878182     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80335  -0.18155  -0.01888   0.19399   0.72918  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.06141    0.02493  -2.463   0.0140 *
## timeNorm     0.03220    0.02896   1.112   0.2664  
## obj.diff     0.08952    0.04587   1.952   0.0513 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06387276)
## 
##     Null deviance: 52.815  on 824  degrees of freedom
## Residual deviance: 52.503  on 822  degrees of freedom
## AIC: 76.782
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.3209524     0.3450617 -0.03697026 150 0.13 :(
##  2:      4.5      0.4266667     0.4418554 -0.01357897  75 0.61 :(
##  3:      7.5      0.4438095     0.4666577 -0.02092597  75 0.44 :(
##  4:     10.5      0.4438095     0.4339237  0.01170254  75 0.74 :(
##  5:     13.5      0.3371429     0.3915256 -0.05898709  75 0.041 *
##  6:     16.5      0.4533333     0.4235337  0.02549517  75 0.27 :(
##  7:     19.5      0.3980952     0.4392064 -0.03752952  75 0.23 :(
##  8:     22.5      0.4133333     0.3941442  0.01243771  75 0.56 :(
##  9:     25.5      0.3961905     0.3735255  0.02575106  75 0.45 :(
## 10:     28.5      0.3180952     0.3321373 -0.01719639  75 0.56 :(
##     time  error.diff shapes
##  1:  1.5 -0.03697026     16
##  2:  4.5 -0.01357897     16
##  3:  7.5 -0.02092597     16
##  4: 10.5  0.01170254     16
##  5: 13.5 -0.05898709     24
##  6: 16.5  0.02549517     16
##  7: 19.5 -0.03752952     16
##  8: 22.5  0.01243771     16
##  9: 25.5  0.02575106     16
## 10: 28.5 -0.01719639     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80489  -0.19715   0.04686   0.12205   0.71041  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10731    0.02825   3.799 0.000161 ***
## timeNorm    -0.01236    0.03779  -0.327 0.743602    
## obj.diff    -0.28137    0.03440  -8.179 1.76e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07744331)
## 
##     Null deviance: 51.071  on 593  degrees of freedom
## Residual deviance: 45.769  on 591  degrees of freedom
## AIC: 171.12
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4060847     0.3647578  0.01440955 108     0.66 :(
##  2:      4.5      0.6481481     0.7356615 -0.10467396  54   0.0024 **
##  3:      7.5      0.5925926     0.7147027 -0.11933066  54   0.0012 **
##  4:     10.5      0.5767196     0.7227008 -0.12067509  54 0.00057 ***
##  5:     13.5      0.5343915     0.6812848 -0.12776766  54 4.4e-06 ***
##  6:     16.5      0.5291005     0.6163897 -0.09974710  54     0.011 *
##  7:     19.5      0.5449735     0.6057373 -0.09050373  54      0.1 :(
##  8:     22.5      0.5449735     0.6322030 -0.11182543  54   0.0041 **
##  9:     25.5      0.5000000     0.5850422 -0.09763863  54 0.00011 ***
## 10:     28.5      0.5846561     0.5894469 -0.04923471  54     0.17 :(
##     time  error.diff shapes
##  1:  1.5  0.01440955     16
##  2:  4.5 -0.10467396     24
##  3:  7.5 -0.11933066     24
##  4: 10.5 -0.12067509     24
##  5: 13.5 -0.12776766     24
##  6: 16.5 -0.09974710     24
##  7: 19.5 -0.09050373     16
##  8: 22.5 -0.11182543     24
##  9: 25.5 -0.09763863     24
## 10: 28.5 -0.04923471     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7546  -0.1203  -0.0149   0.1332   0.8520  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.04967    0.01924  -2.582  0.00998 ** 
## timeNorm     0.02696    0.02862   0.942  0.34649    
## obj.diff    -0.19284    0.02517  -7.662 4.78e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06551003)
## 
##     Null deviance: 62.028  on 890  degrees of freedom
## Residual deviance: 58.173  on 888  degrees of freedom
## AIC: 105.08
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2292769     0.3038003 -0.10333005 162 1.7e-05 ***
##  2:      4.5      0.4550265     0.6435586 -0.15826125  81 1.4e-09 ***
##  3:      7.5      0.3580247     0.5229068 -0.14268518  81 1.3e-07 ***
##  4:     10.5      0.4373898     0.6031879 -0.13652264  81   8e-10 ***
##  5:     13.5      0.3932981     0.5733030 -0.15553550  81   3e-09 ***
##  6:     16.5      0.3544974     0.5345376 -0.15396096  81 1.5e-09 ***
##  7:     19.5      0.4744268     0.5914733 -0.09274510  81 7.1e-06 ***
##  8:     22.5      0.3615520     0.5257793 -0.14843431  81 6.3e-07 ***
##  9:     25.5      0.5061728     0.5797558 -0.08411956  81   7e-04 ***
## 10:     28.5      0.4320988     0.5619786 -0.10644662  81 4.3e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.10333005     24
##  2:  4.5 -0.15826125     24
##  3:  7.5 -0.14268518     24
##  4: 10.5 -0.13652264     24
##  5: 13.5 -0.15553550     24
##  6: 16.5 -0.15396096     24
##  7: 19.5 -0.09274510     24
##  8: 22.5 -0.14843431     24
##  9: 25.5 -0.08411956     24
## 10: 28.5 -0.10644662     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6201  -0.1032  -0.0058   0.1318   0.8614  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03880    0.02941  -1.319    0.188    
## timeNorm     0.02457    0.04594   0.535    0.593    
## obj.diff    -0.20012    0.03979  -5.030 8.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06312685)
## 
##     Null deviance: 22.242  on 329  degrees of freedom
## Residual deviance: 20.642  on 327  degrees of freedom
## AIC: 29.825
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.1690476     0.2398207 -0.07330477 60 5.5e-06 ***
##  2:      4.5      0.4619048     0.5813467 -0.09880129 30    0.004 **
##  3:      7.5      0.3095238     0.4678668 -0.11016732 30 1.2e-05 ***
##  4:     10.5      0.3428571     0.4639707 -0.10959939 30   0.0024 **
##  5:     13.5      0.3142857     0.4613004 -0.11754338 30 0.00046 ***
##  6:     16.5      0.3047619     0.4612711 -0.11813934 30 0.00095 ***
##  7:     19.5      0.3047619     0.4514902 -0.10794597 30 0.00021 ***
##  8:     22.5      0.4142857     0.5050770 -0.08925231 30    0.004 **
##  9:     25.5      0.4238095     0.4813309 -0.06206745 30     0.043 *
## 10:     28.5      0.3380952     0.4606821 -0.10198710 30   0.0081 **
##     time  error.diff shapes
##  1:  1.5 -0.07330477     24
##  2:  4.5 -0.09880129     24
##  3:  7.5 -0.11016732     24
##  4: 10.5 -0.10959939     24
##  5: 13.5 -0.11754338     24
##  6: 16.5 -0.11813934     24
##  7: 19.5 -0.10794597     24
##  8: 22.5 -0.08925231     24
##  9: 25.5 -0.06206745     24
## 10: 28.5 -0.10198710     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6906  -0.2872   0.1618   0.2406   0.4382  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.30476    0.06531   4.666 4.48e-06 ***
## timeNorm     0.08868    0.06179   1.435    0.152    
## obj.diff    -0.68200    0.07366  -9.259  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1146889)
## 
##     Null deviance: 48.122  on 329  degrees of freedom
## Residual deviance: 37.503  on 327  degrees of freedom
## AIC: 226.86
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4690476     0.6190299 -0.155433290 60   0.0022 **
##  2:      4.5      0.5809524     0.8686553 -0.258174644 30 6.3e-05 ***
##  3:      7.5      0.6714286     0.8280425 -0.118687676 30     0.011 *
##  4:     10.5      0.6095238     0.7307282 -0.116673908 30     0.064 .
##  5:     13.5      0.6238095     0.7231805 -0.114980427 30     0.31 :(
##  6:     16.5      0.6142857     0.7634659 -0.122280354 30     0.012 *
##  7:     19.5      0.5619048     0.6841523 -0.079833137 30     0.24 :(
##  8:     22.5      0.4809524     0.6626856 -0.152304784 30   0.0071 **
##  9:     25.5      0.5666667     0.6069621 -0.027205125 30     0.79 :(
## 10:     28.5      0.6238095     0.6142242 -0.008382334 30     0.84 :(
##     time   error.diff shapes
##  1:  1.5 -0.155433290     24
##  2:  4.5 -0.258174644     24
##  3:  7.5 -0.118687676     24
##  4: 10.5 -0.116673908     16
##  5: 13.5 -0.114980427     16
##  6: 16.5 -0.122280354     24
##  7: 19.5 -0.079833137     16
##  8: 22.5 -0.152304784     24
##  9: 25.5 -0.027205125     16
## 10: 28.5 -0.008382334     16
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68259  -0.32180   0.07167   0.25551   0.56027  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.25839    0.03624   7.129 2.96e-12 ***
## timeNorm     0.01121    0.04515   0.248    0.804    
## obj.diff    -0.56174    0.04604 -12.201  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1114018)
## 
##     Null deviance: 82.564  on 593  degrees of freedom
## Residual deviance: 65.838  on 591  degrees of freedom
## AIC: 387.09
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3783069     0.3818097 -0.001249940 108     0.99 :(
##  2:      4.5      0.5846561     0.7600559 -0.180788400  54 8.6e-05 ***
##  3:      7.5      0.5714286     0.6218983 -0.064736939  54     0.17 :(
##  4:     10.5      0.6031746     0.5834307  0.001819149  54     0.95 :(
##  5:     13.5      0.5079365     0.5950296 -0.082973359  54     0.084 .
##  6:     16.5      0.6137566     0.6040416 -0.001443315  54     0.98 :(
##  7:     19.5      0.4470899     0.5492649 -0.116439771  54     0.039 *
##  8:     22.5      0.5132275     0.4793441  0.031002683  54     0.49 :(
##  9:     25.5      0.4497354     0.4559805  0.001828617  54     0.99 :(
## 10:     28.5      0.3968254     0.3909264 -0.008169040  54     0.84 :(
##     time   error.diff shapes
##  1:  1.5 -0.001249940     16
##  2:  4.5 -0.180788400     24
##  3:  7.5 -0.064736939     16
##  4: 10.5  0.001819149     16
##  5: 13.5 -0.082973359     16
##  6: 16.5 -0.001443315     16
##  7: 19.5 -0.116439771     24
##  8: 22.5  0.031002683     16
##  9: 25.5  0.001828617     16
## 10: 28.5 -0.008169040     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6716  -0.2008  -0.1021   0.2271   0.7066  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.141067   0.022046   6.399 2.45e-10 ***
## timeNorm    -0.001129   0.031530  -0.036    0.971    
## obj.diff    -0.402199   0.033874 -11.873  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08679706)
## 
##     Null deviance: 95.199  on 956  degrees of freedom
## Residual deviance: 82.804  on 954  degrees of freedom
## AIC: 381.76
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5      0.2881773     0.2404667 -0.000248799 174 0.99 :(
##  2:      4.5      0.4269294     0.5088613 -0.088531848  87 0.023 *
##  3:      7.5      0.4039409     0.4454615 -0.051697336  87 0.18 :(
##  4:     10.5      0.3924466     0.4450093 -0.054416649  87  0.06 .
##  5:     13.5      0.4055829     0.4084377 -0.004151434  87 0.93 :(
##  6:     16.5      0.3546798     0.3529078 -0.010002837  87 0.73 :(
##  7:     19.5      0.3513957     0.2909555  0.032337112  87 0.39 :(
##  8:     22.5      0.2857143     0.2755358 -0.005896073  87 0.87 :(
##  9:     25.5      0.2824302     0.2656083 -0.010305166  87 0.75 :(
## 10:     28.5      0.2840722     0.2360832  0.004864396  87 0.94 :(
##     time   error.diff shapes
##  1:  1.5 -0.000248799     16
##  2:  4.5 -0.088531848     24
##  3:  7.5 -0.051697336     16
##  4: 10.5 -0.054416649     16
##  5: 13.5 -0.004151434     16
##  6: 16.5 -0.010002837     16
##  7: 19.5  0.032337112     16
##  8: 22.5 -0.005896073     16
##  9: 25.5 -0.010305166     16
## 10: 28.5  0.004864396     16

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with zeroes
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with zeroes

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.150 47 1.8e-05 ***
##  2:      0.09375          0.140 54 5.2e-06 ***
##  3:      0.15625          0.140 58 1.3e-05 ***
##  4:      0.21875          0.100 58 7.1e-05 ***
##  5:      0.28125          0.110 57   1e-05 ***
##  6:      0.34375          0.090 58   0.0014 **
##  7:      0.40625          0.048 58     0.068 .
##  8:      0.46875          0.041 58     0.021 *
##  9:      0.53125         -0.031 58     0.034 *
## 10:      0.59375         -0.052 58     0.018 *
## 11:      0.65625         -0.071 58      0.01 *
## 12:      0.71875         -0.140 58 1.3e-06 ***
## 13:      0.78125         -0.170 58 6.7e-09 ***
## 14:      0.84375         -0.220 58 6.1e-09 ***
## 15:      0.90625         -0.230 57 2.1e-10 ***
## 16:      0.96875         -0.190 55 6.8e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 47 1.8e-05 ***
##  2: 54 5.2e-06 ***
##  3: 58 1.3e-05 ***
##  4: 58 7.1e-05 ***
##  5: 57   1e-05 ***
##  6: 58   0.0014 **
##  7: 58     0.068 .
##  8: 58     0.021 *
##  9: 58     0.034 *
## 10: 58     0.018 *
## 11: 58      0.01 *
## 12: 58 1.3e-06 ***
## 13: 58 6.7e-09 ***
## 14: 58 6.1e-09 ***
## 15: 57 2.1e-10 ***
## 16: 55 6.8e-09 ***
## [1] 56.8
## [1] 2.86

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.090 32   0.0024 **
##  2:      0.09375          0.110 34   0.0021 **
##  3:      0.15625          0.100 42   0.0076 **
##  4:      0.21875          0.110 40   0.0042 **
##  5:      0.28125          0.110 38   0.0044 **
##  6:      0.34375          0.110 37   0.0043 **
##  7:      0.40625          0.056 36     0.037 *
##  8:      0.46875          0.056 38     0.039 *
##  9:      0.53125          0.026 40      0.5 :(
## 10:      0.59375         -0.027 39     0.45 :(
## 11:      0.65625         -0.023 35     0.41 :(
## 12:      0.71875         -0.150 37 0.00038 ***
## 13:      0.78125         -0.170 37 0.00037 ***
## 14:      0.84375         -0.250 29   3e-05 ***
## 15:      0.90625         -0.230 22 0.00031 ***
## 16:      0.96875         -0.100 11     0.036 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32   0.0024 **
##  2: 34   0.0021 **
##  3: 42   0.0076 **
##  4: 40   0.0042 **
##  5: 38   0.0044 **
##  6: 37   0.0043 **
##  7: 36     0.037 *
##  8: 38     0.039 *
##  9: 40      0.5 :(
## 10: 39     0.45 :(
## 11: 35     0.41 :(
## 12: 37 0.00038 ***
## 13: 37 0.00037 ***
## 14: 29   3e-05 ***
## 15: 22 0.00031 ***
## 16: 11     0.036 *
## [1] 34.2
## [1] 7.86

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0860 24     0.19 :(
##  2:      0.09375         0.1600 33 0.00098 ***
##  3:      0.15625         0.0940 39     0.019 *
##  4:      0.21875         0.0520 43     0.089 .
##  5:      0.28125         0.1200 43     0.025 *
##  6:      0.34375         0.0650 37     0.23 :(
##  7:      0.40625         0.0028 44     0.97 :(
##  8:      0.46875         0.0012 42        1 :(
##  9:      0.53125        -0.0480 41   0.0078 **
## 10:      0.59375        -0.0940 38     0.012 *
## 11:      0.65625        -0.1600 42 0.00054 ***
## 12:      0.71875        -0.1600 41 0.00073 ***
## 13:      0.78125        -0.1800 43 5.9e-06 ***
## 14:      0.84375        -0.2400 42 4.4e-07 ***
## 15:      0.90625        -0.2600 39 2.5e-07 ***
## 16:      0.96875        -0.2200 37 1.2e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 24     0.19 :(
##  2: 33 0.00098 ***
##  3: 39     0.019 *
##  4: 43     0.089 .
##  5: 43     0.025 *
##  6: 37     0.23 :(
##  7: 44     0.97 :(
##  8: 42        1 :(
##  9: 41   0.0078 **
## 10: 38     0.012 *
## 11: 42 0.00054 ***
## 12: 41 0.00073 ***
## 13: 43 5.9e-06 ***
## 14: 42 4.4e-07 ***
## 15: 39 2.5e-07 ***
## 16: 37 1.2e-05 ***
## [1] 39.2
## [1] 5.01

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  3          NA
##  2:      0.09375          0.160 12     0.053 .
##  3:      0.15625          0.270 19   0.0011 **
##  4:      0.21875          0.160 20   0.0081 **
##  5:      0.28125          0.094 20     0.14 :(
##  6:      0.34375          0.160 21     0.033 *
##  7:      0.40625          0.094 21     0.091 .
##  8:      0.46875          0.069 19     0.086 .
##  9:      0.53125         -0.031 17     0.026 *
## 10:      0.59375         -0.077 22     0.11 :(
## 11:      0.65625         -0.160 21     0.26 :(
## 12:      0.71875         -0.200 24   0.0013 **
## 13:      0.78125         -0.240 24 0.00026 ***
## 14:      0.84375         -0.220 25   2e-04 ***
## 15:      0.90625         -0.210 25 0.00015 ***
## 16:      0.96875         -0.280 25 1.5e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 12     0.053 .
##  2: 19   0.0011 **
##  3: 20   0.0081 **
##  4: 20     0.14 :(
##  5: 21     0.033 *
##  6: 21     0.091 .
##  7: 19     0.086 .
##  8: 17     0.026 *
##  9: 22     0.11 :(
## 10: 21     0.26 :(
## 11: 24   0.0013 **
## 12: 24 0.00026 ***
## 13: 25   2e-04 ***
## 14: 25 0.00015 ***
## 15: 25 1.5e-05 ***
## [1] 21
## [1] 3.53
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.160  8     0.29 :(
##  3:      0.15625          0.094 29     0.22 :(
##  4:      0.21875          0.056 41     0.063 .
##  5:      0.28125          0.085 48      0.01 *
##  6:      0.34375          0.069 50     0.034 *
##  7:      0.40625          0.069 50     0.086 .
##  8:      0.46875          0.044 54      0.03 *
##  9:      0.53125          0.019 52     0.66 :(
## 10:      0.59375         -0.019 51     0.65 :(
## 11:      0.65625         -0.040 52     0.11 :(
## 12:      0.71875         -0.069 53   0.0037 **
## 13:      0.78125         -0.110 46 0.00082 ***
## 14:      0.84375         -0.170 29   0.0015 **
## 15:      0.90625         -0.180 13     0.056 .
## 16:      0.96875         -0.270  6     0.052 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.29 :(
##  2: 29     0.22 :(
##  3: 41     0.063 .
##  4: 48      0.01 *
##  5: 50     0.034 *
##  6: 50     0.086 .
##  7: 54      0.03 *
##  8: 52     0.66 :(
##  9: 51     0.65 :(
## 10: 52     0.11 :(
## 11: 53   0.0037 **
## 12: 46 0.00082 ***
## 13: 29   0.0015 **
## 14: 13     0.056 .
## 15:  6     0.052 .
## [1] 38.8
## [1] 17.3
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375         0.1600  8 0.29 :(
##  3:      0.15625         0.0690 24 0.45 :(
##  4:      0.21875         0.0560 25 0.15 :(
##  5:      0.28125         0.0690 25 0.071 .
##  6:      0.34375         0.0560 25 0.089 .
##  7:      0.40625         0.0810 24 0.098 .
##  8:      0.46875         0.0940 24 0.031 *
##  9:      0.53125         0.0680 23 0.21 :(
## 10:      0.59375         0.0560 22 0.28 :(
## 11:      0.65625        -0.0062 22    1 :(
## 12:      0.71875        -0.0690 21 0.11 :(
## 13:      0.78125        -0.0650 15 0.081 .
## 14:      0.84375             NA  2      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  8 0.29 :(
##  2: 24 0.45 :(
##  3: 25 0.15 :(
##  4: 25 0.071 .
##  5: 25 0.089 .
##  6: 24 0.098 .
##  7: 24 0.031 *
##  8: 23 0.21 :(
##  9: 22 0.28 :(
## 10: 22    1 :(
## 11: 21 0.11 :(
## 12: 15 0.081 .
## [1] 21.5
## [1] 5.07
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625          0.290  5 0.28 :(
##  4:      0.21875          0.048 16 0.22 :(
##  5:      0.28125          0.120 23  0.07 .
##  6:      0.34375          0.069 24 0.26 :(
##  7:      0.40625          0.054 25 0.45 :(
##  8:      0.46875          0.031 25 0.25 :(
##  9:      0.53125         -0.028 25 0.63 :(
## 10:      0.59375         -0.069 22 0.23 :(
## 11:      0.65625         -0.130 23 0.019 *
## 12:      0.71875         -0.069 25 0.084 .
## 13:      0.78125         -0.120 25 0.013 *
## 14:      0.84375         -0.170 20 0.024 *
## 15:      0.90625         -0.170  6 0.14 :(
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.28 :(
##  2: 16 0.22 :(
##  3: 23  0.07 .
##  4: 24 0.26 :(
##  5: 25 0.45 :(
##  6: 25 0.25 :(
##  7: 25 0.63 :(
##  8: 22 0.23 :(
##  9: 23 0.019 *
## 10: 25 0.084 .
## 11: 25 0.013 *
## 12: 20 0.024 *
## 13:  6 0.14 :(
## [1] 20.3
## [1] 7.06
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875          0.031 5 0.58 :(
##  9:      0.53125             NA 4      NA
## 10:      0.59375         -0.050 7  0.1 :(
## 11:      0.65625         -0.019 7 0.93 :(
## 12:      0.71875         -0.120 7 0.15 :(
## 13:      0.78125         -0.130 6 0.14 :(
## 14:      0.84375         -0.170 7 0.11 :(
## 15:      0.90625         -0.200 7 0.27 :(
## 16:      0.96875         -0.270 6 0.052 .
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  5 0.58 :(
## 2:  7  0.1 :(
## 3:  7 0.93 :(
## 4:  7 0.15 :(
## 5:  6 0.14 :(
## 6:  7 0.11 :(
## 7:  7 0.27 :(
## 8:  6 0.052 .
## [1] 6.5
## [1] 0.756
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0490 32     0.069 .
##  2:      0.09375         0.1200 48   0.0073 **
##  3:      0.15625         0.0940 51     0.015 *
##  4:      0.21875         0.0310 47     0.34 :(
##  5:      0.28125         0.0020 46     0.97 :(
##  6:      0.34375        -0.0440 41     0.67 :(
##  7:      0.40625        -0.0063 44     0.77 :(
##  8:      0.46875        -0.0190 42     0.63 :(
##  9:      0.53125        -0.1600 34   0.0078 **
## 10:      0.59375        -0.2400 37 0.00021 ***
## 11:      0.65625        -0.1600 40 0.00065 ***
## 12:      0.71875        -0.2200 46 1.8e-06 ***
## 13:      0.78125        -0.2700 42 3.2e-06 ***
## 14:      0.84375        -0.2400 46 3.4e-07 ***
## 15:      0.90625        -0.2200 53 4.6e-08 ***
## 16:      0.96875        -0.1300 55 3.7e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.069 .
##  2: 48   0.0073 **
##  3: 51     0.015 *
##  4: 47     0.34 :(
##  5: 46     0.97 :(
##  6: 41     0.67 :(
##  7: 44     0.77 :(
##  8: 42     0.63 :(
##  9: 34   0.0078 **
## 10: 37 0.00021 ***
## 11: 40 0.00065 ***
## 12: 46 1.8e-06 ***
## 13: 42 3.2e-06 ***
## 14: 46 3.4e-07 ***
## 15: 53 4.6e-08 ***
## 16: 55 3.7e-07 ***
## [1] 44
## [1] 6.4

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125         0.0810 10 0.041 *
##  2:      0.09375         0.0062 10    1 :(
##  3:      0.15625         0.0940  9 0.81 :(
##  4:      0.21875        -0.0190  5    1 :(
##  5:      0.28125         0.0022  8 0.83 :(
##  6:      0.34375         0.0063  6 0.83 :(
##  7:      0.40625        -0.1100  7 0.55 :(
##  8:      0.46875        -0.1200  9 0.23 :(
##  9:      0.53125        -0.0430  7 0.45 :(
## 10:      0.59375        -0.2400  6 0.21 :(
## 11:      0.65625        -0.2100  5 0.28 :(
## 12:      0.71875        -0.3400  9 0.012 *
## 13:      0.78125        -0.2800  7 0.051 .
## 14:      0.84375        -0.2400  7 0.074 .
## 15:      0.90625        -0.1600  9 0.075 .
## 16:      0.96875        -0.0850 10 0.18 :(
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 10 0.041 *
##  2: 10    1 :(
##  3:  9 0.81 :(
##  4:  5    1 :(
##  5:  8 0.83 :(
##  6:  6 0.83 :(
##  7:  7 0.55 :(
##  8:  9 0.23 :(
##  9:  7 0.45 :(
## 10:  6 0.21 :(
## 11:  5 0.28 :(
## 12:  9 0.012 *
## 13:  7 0.051 .
## 14:  7 0.074 .
## 15:  9 0.075 .
## 16: 10 0.18 :(
## [1] 7.75
## [1] 1.73

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0190 19     0.88 :(
##  2:      0.09375         0.0900 27     0.028 *
##  3:      0.15625         0.0021 26        1 :(
##  4:      0.21875        -0.0520 24     0.46 :(
##  5:      0.28125        -0.0310 22     0.34 :(
##  6:      0.34375        -0.0570 19     0.45 :(
##  7:      0.40625        -0.0560 22     0.36 :(
##  8:      0.46875        -0.0190 23      0.7 :(
##  9:      0.53125        -0.2600 18     0.013 *
## 10:      0.59375        -0.2400 20   0.0073 **
## 11:      0.65625        -0.1600 22     0.026 *
## 12:      0.71875        -0.2200 21   0.0016 **
## 13:      0.78125        -0.1900 19      0.01 *
## 14:      0.84375        -0.2700 24 0.00018 ***
## 15:      0.90625        -0.2300 27   5e-05 ***
## 16:      0.96875        -0.0960 27   0.0023 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19     0.88 :(
##  2: 27     0.028 *
##  3: 26        1 :(
##  4: 24     0.46 :(
##  5: 22     0.34 :(
##  6: 19     0.45 :(
##  7: 22     0.36 :(
##  8: 23      0.7 :(
##  9: 18     0.013 *
## 10: 20   0.0073 **
## 11: 22     0.026 *
## 12: 21   0.0016 **
## 13: 19      0.01 *
## 14: 24 0.00018 ***
## 15: 27   5e-05 ***
## 16: 27   0.0023 **
## [1] 22.5
## [1] 3.1

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  3          NA
##  2:      0.09375          0.160 11     0.035 *
##  3:      0.15625          0.270 16   0.0023 **
##  4:      0.21875          0.110 18     0.032 *
##  5:      0.28125          0.035 16     0.36 :(
##  6:      0.34375         -0.028 16        1 :(
##  7:      0.40625          0.094 15     0.067 .
##  8:      0.46875          0.031 10      0.3 :(
##  9:      0.53125         -0.031  9      0.4 :(
## 10:      0.59375         -0.170 11     0.026 *
## 11:      0.65625         -0.160 13     0.017 *
## 12:      0.71875         -0.220 16      0.01 *
## 13:      0.78125         -0.280 16   0.0013 **
## 14:      0.84375         -0.240 15   0.0029 **
## 15:      0.90625         -0.240 17   0.0018 **
## 16:      0.96875         -0.220 18 0.00032 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.035 *
##  2: 16   0.0023 **
##  3: 18     0.032 *
##  4: 16     0.36 :(
##  5: 16        1 :(
##  6: 15     0.067 .
##  7: 10      0.3 :(
##  8:  9      0.4 :(
##  9: 11     0.026 *
## 10: 13     0.017 *
## 11: 16      0.01 *
## 12: 16   0.0013 **
## 13: 15   0.0029 **
## 14: 17   0.0018 **
## 15: 18 0.00032 ***
## [1] 14.5
## [1] 2.92
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.1400 38   0.0033 **
##  2:      0.09375         0.1800 43 8.1e-06 ***
##  3:      0.15625         0.2000 48 3.1e-06 ***
##  4:      0.21875         0.1900 50   2e-06 ***
##  5:      0.28125         0.2200 49 6.1e-05 ***
##  6:      0.34375         0.1800 41 2.3e-05 ***
##  7:      0.40625         0.0940 47     0.011 *
##  8:      0.46875         0.0310 47   0.0053 **
##  9:      0.53125        -0.0310 45     0.087 .
## 10:      0.59375         0.0062 46     0.86 :(
## 11:      0.65625        -0.0810 42     0.15 :(
## 12:      0.71875        -0.1700 41   0.0011 **
## 13:      0.78125        -0.1600 53 7.2e-06 ***
## 14:      0.84375        -0.2400 52 1.4e-08 ***
## 15:      0.90625        -0.2600 42 1.2e-07 ***
## 16:      0.96875        -0.3900 29 2.6e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38   0.0033 **
##  2: 43 8.1e-06 ***
##  3: 48 3.1e-06 ***
##  4: 50   2e-06 ***
##  5: 49 6.1e-05 ***
##  6: 41 2.3e-05 ***
##  7: 47     0.011 *
##  8: 47   0.0053 **
##  9: 45     0.087 .
## 10: 46     0.86 :(
## 11: 42     0.15 :(
## 12: 41   0.0011 **
## 13: 53 7.2e-06 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.2e-07 ***
## 16: 29 2.6e-06 ***
## [1] 44.6
## [1] 5.93

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.094 29   0.032 *
##  2:      0.09375          0.140 29 4e-04 ***
##  3:      0.15625          0.140 28 0.0017 **
##  4:      0.21875          0.160 26 0.0036 **
##  5:      0.28125          0.180 24   0.039 *
##  6:      0.34375          0.160 21   0.022 *
##  7:      0.40625          0.069 21   0.14 :(
##  8:      0.46875          0.031 25   0.076 .
##  9:      0.53125         -0.031 23   0.49 :(
## 10:      0.59375         -0.094 23   0.12 :(
## 11:      0.65625         -0.056 22   0.45 :(
## 12:      0.71875         -0.160 20   0.037 *
## 13:      0.78125         -0.160 27 0.0018 **
## 14:      0.84375         -0.250 25 9e-05 ***
## 15:      0.90625         -0.310 15 0.0024 **
## 16:      0.96875         -0.240  2    0.5 :(
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 29   0.032 *
##  2: 29 4e-04 ***
##  3: 28 0.0017 **
##  4: 26 0.0036 **
##  5: 24   0.039 *
##  6: 21   0.022 *
##  7: 21   0.14 :(
##  8: 25   0.076 .
##  9: 23   0.49 :(
## 10: 23   0.12 :(
## 11: 22   0.45 :(
## 12: 20   0.037 *
## 13: 27 0.0018 **
## 14: 25 9e-05 ***
## 15: 15 0.0024 **
## 16:  2    0.5 :(
## [1] 22.5
## [1] 6.58

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.220  9     0.055 .
##  2:      0.09375          0.310 13   0.0038 **
##  3:      0.15625          0.340 16   0.0011 **
##  4:      0.21875          0.210 18    0.005 **
##  5:      0.28125          0.220 17   0.0055 **
##  6:      0.34375          0.160 14   0.0029 **
##  7:      0.40625          0.094 16     0.044 *
##  8:      0.46875          0.031 14     0.36 :(
##  9:      0.53125         -0.031 14      0.3 :(
## 10:      0.59375          0.081 14     0.11 :(
## 11:      0.65625         -0.160 13     0.024 *
## 12:      0.71875         -0.170 15     0.078 .
## 13:      0.78125         -0.210 17   0.0014 **
## 14:      0.84375         -0.260 17 0.00049 ***
## 15:      0.90625         -0.270 17 0.00041 ***
## 16:      0.96875         -0.420 17   3e-04 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.055 .
##  2: 13   0.0038 **
##  3: 16   0.0011 **
##  4: 18    0.005 **
##  5: 17   0.0055 **
##  6: 14   0.0029 **
##  7: 16     0.044 *
##  8: 14     0.36 :(
##  9: 14      0.3 :(
## 10: 14     0.11 :(
## 11: 13     0.024 *
## 12: 15     0.078 .
## 13: 17   0.0014 **
## 14: 17 0.00049 ***
## 15: 17 0.00041 ***
## 16: 17   3e-04 ***
## [1] 15.1
## [1] 2.29

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625          0.290  4   0.58 :(
##  4:      0.21875          0.280  6   0.034 *
##  5:      0.28125          0.290  8   0.041 *
##  6:      0.34375          0.410  6   0.036 *
##  7:      0.40625          0.094 10   0.41 :(
##  8:      0.46875          0.130  8 0.0078 **
##  9:      0.53125         -0.031  8   0.098 .
## 10:      0.59375          0.056  9   0.34 :(
## 11:      0.65625          0.077  7   0.55 :(
## 12:      0.71875         -0.170  6   0.058 .
## 13:      0.78125         -0.056  9    0.4 :(
## 14:      0.84375         -0.240 10   0.032 *
## 15:      0.90625         -0.240 10   0.019 *
## 16:      0.96875         -0.380 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  4   0.58 :(
##  2:  6   0.034 *
##  3:  8   0.041 *
##  4:  6   0.036 *
##  5: 10   0.41 :(
##  6:  8 0.0078 **
##  7:  8   0.098 .
##  8:  9   0.34 :(
##  9:  7   0.55 :(
## 10:  6   0.058 .
## 11:  9    0.4 :(
## 12: 10   0.032 *
## 13: 10   0.019 *
## 14: 10 0.0059 **
## [1] 7.93
## [1] 1.9
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71942  -0.17247   0.00783   0.17452   0.64036  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.168653   0.015973   10.56   <2e-16 ***
## timeNorm     0.007875   0.017892    0.44     0.66    
## obj.diff    -0.353796   0.024499  -14.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05568373)
## 
##     Null deviance: 116.20  on 1880  degrees of freedom
## Residual deviance: 104.57  on 1878  degrees of freedom
## AIC: -89.41
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78054  -0.20402  -0.04881   0.24891   0.83542  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.09780    0.01419   6.890 7.67e-12 ***
## timeNorm     0.04961    0.02071   2.395   0.0167 *  
## obj.diff    -0.35946    0.01812 -19.840  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07049446)
## 
##     Null deviance: 155.50  on 1814  degrees of freedom
## Residual deviance: 127.74  on 1812  degrees of freedom
## AIC: 341.96
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71114  -0.19255  -0.00268   0.18201   0.72417  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.24232    0.01477  16.412  < 2e-16 ***
## timeNorm     0.05282    0.01983   2.664  0.00779 ** 
## obj.diff    -0.56482    0.01910 -29.567  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06780909)
## 
##     Null deviance: 188.61  on 1880  degrees of freedom
## Residual deviance: 127.35  on 1878  degrees of freedom
## AIC: 281.16
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.4438596     0.4457102  0.0017643322 342 0.89 :(
##  2:      4.5      0.5374269     0.5624859 -0.0193532613 171 0.35 :(
##  3:      7.5      0.5157895     0.5357049 -0.0159697343 171 0.41 :(
##  4:     10.5      0.5421053     0.5362058  0.0148489779 171 0.46 :(
##  5:     13.5      0.5157895     0.5133937  0.0051408312 171  0.8 :(
##  6:     16.5      0.5315789     0.5320036  0.0012773881 171 0.95 :(
##  7:     19.5      0.5064327     0.5358363 -0.0298109502 171  0.1 :(
##  8:     22.5      0.4871345     0.4961373 -0.0092826000 171 0.66 :(
##  9:     25.5      0.4888889     0.4868060 -0.0006626257 171 0.98 :(
## 10:     28.5      0.4736842     0.4657574  0.0047061353 171 0.81 :(
##     time    error.diff shapes
##  1:  1.5  0.0017643322     16
##  2:  4.5 -0.0193532613     16
##  3:  7.5 -0.0159697343     16
##  4: 10.5  0.0148489779     16
##  5: 13.5  0.0051408312     16
##  6: 16.5  0.0012773881     16
##  7: 19.5 -0.0298109502     16
##  8: 22.5 -0.0092826000     16
##  9: 25.5 -0.0006626257     16
## 10: 28.5  0.0047061353     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3309091     0.3121174  0.01322078 330     0.53 :(
##  2:      4.5      0.5115152     0.6623901 -0.14555654 165 5.5e-10 ***
##  3:      7.5      0.4606061     0.5756691 -0.11526777 165 1.3e-06 ***
##  4:     10.5      0.5145455     0.6169890 -0.10480311 165 7.5e-06 ***
##  5:     13.5      0.4721212     0.5882784 -0.10892892 165 1.7e-07 ***
##  6:     16.5      0.4327273     0.5480044 -0.12509592 165   6e-07 ***
##  7:     19.5      0.4824242     0.5706900 -0.08307269 165 2.6e-05 ***
##  8:     22.5      0.5018182     0.5568448 -0.05086146 165     0.019 *
##  9:     25.5      0.5424242     0.5635905 -0.01007155 165     0.61 :(
## 10:     28.5      0.5084848     0.5525507 -0.04380158 165     0.048 *
##     time  error.diff shapes
##  1:  1.5  0.01322078     16
##  2:  4.5 -0.14555654     24
##  3:  7.5 -0.11526777     24
##  4: 10.5 -0.10480311     24
##  5: 13.5 -0.10892892     24
##  6: 16.5 -0.12509592     24
##  7: 19.5 -0.08307269     24
##  8: 22.5 -0.05086146     24
##  9: 25.5 -0.01007155     16
## 10: 28.5 -0.04380158     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3938596     0.3515160  0.050094933 342     0.016 *
##  2:      4.5      0.5081871     0.6513076 -0.158414133 171 7.7e-09 ***
##  3:      7.5      0.5093567     0.5682979 -0.068502095 171   0.0063 **
##  4:     10.5      0.5204678     0.5388474 -0.021601840 171     0.38 :(
##  5:     13.5      0.5157895     0.5225795 -0.010574437 171     0.66 :(
##  6:     16.5      0.5093567     0.5042410 -0.004080511 171     0.87 :(
##  7:     19.5      0.4614035     0.4415088  0.014455090 171     0.54 :(
##  8:     22.5      0.4280702     0.4078173  0.014864320 171     0.56 :(
##  9:     25.5      0.4614035     0.3856125  0.082930837 171   0.0017 **
## 10:     28.5      0.4485380     0.3513216  0.090016969 171 0.00063 ***
##     time   error.diff shapes
##  1:  1.5  0.050094933     24
##  2:  4.5 -0.158414133     24
##  3:  7.5 -0.068502095     24
##  4: 10.5 -0.021601840     16
##  5: 13.5 -0.010574437     16
##  6: 16.5 -0.004080511     16
##  7: 19.5  0.014455090     16
##  8: 22.5  0.014864320     16
##  9: 25.5  0.082930837     24
## 10: 28.5  0.090016969     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.75694  -0.18360  -0.03019   0.21428   0.58010  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.26136    0.02137  12.227  < 2e-16 ***
## timeNorm     0.08303    0.02434   3.412 0.000668 ***
## obj.diff    -0.58091    0.02545 -22.829  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06314505)
## 
##     Null deviance: 106.026  on 1154  degrees of freedom
## Residual deviance:  72.743  on 1152  degrees of freedom
## AIC: 92.263
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72990  -0.21256  -0.00011   0.21367   0.76833  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17318    0.01398  12.387   <2e-16 ***
## timeNorm     0.04440    0.01810   2.452   0.0143 *  
## obj.diff    -0.43955    0.01914 -22.960   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06970934)
## 
##     Null deviance: 197.63  on 2309  degrees of freedom
## Residual deviance: 160.82  on 2307  degrees of freedom
## AIC: 407.99
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68710  -0.20239  -0.00475   0.20535   0.79944  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15361    0.01286  11.942   <2e-16 ***
## timeNorm     0.03029    0.01787   1.695   0.0903 .  
## obj.diff    -0.37043    0.02033 -18.218   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06229074)
## 
##     Null deviance: 152.40  on 2111  degrees of freedom
## Residual deviance: 131.37  on 2109  degrees of freedom
## AIC: 135.8
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4909524     0.5078590 -0.02461793 210     0.31 :(
##  2:      4.5      0.5714286     0.7854497 -0.22469049 105 2.1e-10 ***
##  3:      7.5      0.5980952     0.7571408 -0.17682529 105 1.6e-07 ***
##  4:     10.5      0.6428571     0.7378391 -0.10178489 105   0.0023 **
##  5:     13.5      0.5895238     0.7194038 -0.15488488 105 8.6e-06 ***
##  6:     16.5      0.5780952     0.6928809 -0.12865340 105 0.00019 ***
##  7:     19.5      0.5895238     0.6519999 -0.06758259 105     0.012 *
##  8:     22.5      0.5980952     0.6672242 -0.06606931 105     0.025 *
##  9:     25.5      0.6019048     0.6374488 -0.03868620 105      0.3 :(
## 10:     28.5      0.6161905     0.6321308 -0.01384501 105     0.64 :(
##     time  error.diff shapes
##  1:  1.5 -0.02461793     16
##  2:  4.5 -0.22469049     24
##  3:  7.5 -0.17682529     24
##  4: 10.5 -0.10178489     24
##  5: 13.5 -0.15488488     24
##  6: 16.5 -0.12865340     24
##  7: 19.5 -0.06758259     24
##  8: 22.5 -0.06606931     24
##  9: 25.5 -0.03868620     16
## 10: 28.5 -0.01384501     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3892857     0.3833571  0.009187871 420     0.55 :(
##  2:      4.5      0.5438095     0.6644303 -0.118334829 210 5.7e-09 ***
##  3:      7.5      0.5009524     0.5546648 -0.057738067 210   0.0055 **
##  4:     10.5      0.5323810     0.5856428 -0.055862994 210     0.012 *
##  5:     13.5      0.5238095     0.5711545 -0.049345354 210     0.013 *
##  6:     16.5      0.5080952     0.5645710 -0.059023543 210   0.0031 **
##  7:     19.5      0.5085714     0.5763408 -0.070409217 210 0.00038 ***
##  8:     22.5      0.4742857     0.5129160 -0.047311259 210     0.034 *
##  9:     25.5      0.5309524     0.5222933  0.008175683 210      0.7 :(
## 10:     28.5      0.4976190     0.5011792 -0.015917435 210     0.43 :(
##     time   error.diff shapes
##  1:  1.5  0.009187871     16
##  2:  4.5 -0.118334829     24
##  3:  7.5 -0.057738067     24
##  4: 10.5 -0.055862994     24
##  5: 13.5 -0.049345354     24
##  6: 16.5 -0.059023543     24
##  7: 19.5 -0.070409217     24
##  8: 22.5 -0.047311259     24
##  9: 25.5  0.008175683     16
## 10: 28.5 -0.015917435     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3361979     0.2812232  0.05805815 384 0.00043 ***
##  2:      4.5      0.4635417     0.4940130 -0.02971391 192     0.17 :(
##  3:      7.5      0.4338542     0.4572421 -0.01981303 192     0.34 :(
##  4:     10.5      0.4546875     0.4436417  0.01569938 192     0.39 :(
##  5:     13.5      0.4291667     0.4100912  0.02898757 192     0.17 :(
##  6:     16.5      0.4270833     0.3974278  0.02804448 192      0.2 :(
##  7:     19.5      0.3979167     0.3739496  0.02162764 192     0.31 :(
##  8:     22.5      0.4005208     0.3577330  0.04141614 192     0.033 *
##  9:     25.5      0.4026042     0.3414701  0.05789652 192   0.0017 **
## 10:     28.5      0.3770833     0.3086979  0.06005258 192    0.002 **
##     time  error.diff shapes
##  1:  1.5  0.05805815     24
##  2:  4.5 -0.02971391     16
##  3:  7.5 -0.01981303     16
##  4: 10.5  0.01569938     16
##  5: 13.5  0.02898757     16
##  6: 16.5  0.02804448     16
##  7: 19.5  0.02162764     16
##  8: 22.5  0.04141614     24
##  9: 25.5  0.05789652     24
## 10: 28.5  0.06005258     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68318  -0.17197  -0.06763   0.20953   0.44308  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.26214    0.07667   3.419 0.000745 ***
## timeNorm     0.03922    0.04490   0.873 0.383385    
## obj.diff    -0.52604    0.09657  -5.448 1.32e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04276234)
## 
##     Null deviance: 11.0260  on 230  degrees of freedom
## Residual deviance:  9.7498  on 228  degrees of freedom
## AIC: -67.605
## 
## Number of Fisher Scoring iterations: 2
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5928571     0.7170177 -0.13220955 42 0.0011 **
##  2:      4.5      0.6571429     0.7946114 -0.14995732 21   0.019 *
##  3:      7.5      0.6380952     0.7649789 -0.12991685 21   0.035 *
##  4:     10.5      0.6571429     0.7869246 -0.13826606 21   0.026 *
##  5:     13.5      0.6428571     0.8120284 -0.18251913 21 0.0016 **
##  6:     16.5      0.6571429     0.7887369 -0.14411936 21    0.06 .
##  7:     19.5      0.6761905     0.7250289 -0.04778299 21   0.29 :(
##  8:     22.5      0.6666667     0.7637626 -0.10072169 21    0.1 :(
##  9:     25.5      0.6809524     0.8157609 -0.13535368 21   0.013 *
## 10:     28.5      0.6333333     0.7674702 -0.11749764 21   0.042 *
##     time  error.diff shapes
##  1:  1.5 -0.13220955     24
##  2:  4.5 -0.14995732     24
##  3:  7.5 -0.12991685     24
##  4: 10.5 -0.13826606     24
##  5: 13.5 -0.18251913     24
##  6: 16.5 -0.14411936     16
##  7: 19.5 -0.04778299     16
##  8: 22.5 -0.10072169     16
##  9: 25.5 -0.13535368     24
## 10: 28.5 -0.11749764     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70317  -0.16896   0.01375   0.17698   0.64802  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18709    0.02966   6.307 4.63e-10 ***
## timeNorm    -0.01154    0.02965  -0.389    0.697    
## obj.diff    -0.38631    0.04612  -8.375 2.36e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0670456)
## 
##     Null deviance: 59.836  on 824  degrees of freedom
## Residual deviance: 55.111  on 822  degrees of freedom
## AIC: 116.78
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n     pval
##  1:      1.5      0.4600000     0.4703926 -0.004017585 150  0.91 :(
##  2:      4.5      0.5626667     0.6181213 -0.049929670  75  0.12 :(
##  3:      7.5      0.5386667     0.5405554  0.007318205  75  0.84 :(
##  4:     10.5      0.5426667     0.5682867 -0.012295430  75  0.75 :(
##  5:     13.5      0.5506667     0.5516441 -0.004940452  75  0.87 :(
##  6:     16.5      0.5440000     0.5685882 -0.030883251  75  0.31 :(
##  7:     19.5      0.4946667     0.5794923 -0.085106719  75 0.003 **
##  8:     22.5      0.4706667     0.5231952 -0.058919282  75  0.072 .
##  9:     25.5      0.5013333     0.5079792 -0.014719847  75  0.71 :(
## 10:     28.5      0.5013333     0.5148979 -0.027308081  75  0.35 :(
##     time   error.diff shapes
##  1:  1.5 -0.004017585     16
##  2:  4.5 -0.049929670     16
##  3:  7.5  0.007318205     16
##  4: 10.5 -0.012295430     16
##  5: 13.5 -0.004940452     16
##  6: 16.5 -0.030883251     16
##  7: 19.5 -0.085106719     24
##  8: 22.5 -0.058919282     16
##  9: 25.5 -0.014719847     16
## 10: 28.5 -0.027308081     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61865  -0.16483   0.01371   0.17764   0.56340  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11206    0.02131   5.260 1.84e-07 ***
## timeNorm     0.02484    0.02475   1.004    0.316    
## obj.diff    -0.19452    0.03920  -4.963 8.45e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04664526)
## 
##     Null deviance: 39.558  on 824  degrees of freedom
## Residual deviance: 38.342  on 822  degrees of freedom
## AIC: -182.53
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.3860000     0.3450617  0.042653637 150   0.021 *
##  2:      4.5      0.4786667     0.4418554  0.043104906  75   0.14 :(
##  3:      7.5      0.4586667     0.4666577 -0.005389581  75   0.86 :(
##  4:     10.5      0.5093333     0.4339237  0.083907158  75 0.0024 **
##  5:     13.5      0.4453333     0.3915256  0.062218949  75   0.021 *
##  6:     16.5      0.4840000     0.4235337  0.063525179  75   0.015 *
##  7:     19.5      0.4706667     0.4392064  0.031920568  75   0.25 :(
##  8:     22.5      0.4533333     0.3941442  0.062224272  75   0.026 *
##  9:     25.5      0.4226667     0.3735255  0.050458512  75    0.05 .
## 10:     28.5      0.4013333     0.3321373  0.066488010  75 0.0078 **
##     time   error.diff shapes
##  1:  1.5  0.042653637     24
##  2:  4.5  0.043104906     16
##  3:  7.5 -0.005389581     16
##  4: 10.5  0.083907158     24
##  5: 13.5  0.062218949     24
##  6: 16.5  0.063525179     24
##  7: 19.5  0.031920568     16
##  8: 22.5  0.062224272     24
##  9: 25.5  0.050458512     16
## 10: 28.5  0.066488010     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74353  -0.20874   0.00921   0.20654   0.60833  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22508    0.02572   8.752   <2e-16 ***
## timeNorm     0.03968    0.03440   1.154    0.249    
## obj.diff    -0.51555    0.03132 -16.460   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06419126)
## 
##     Null deviance: 55.359  on 593  degrees of freedom
## Residual deviance: 37.937  on 591  degrees of freedom
## AIC: 59.634
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4361111     0.3647578  0.07303014 108     0.039 *
##  2:      4.5      0.5592593     0.7356615 -0.18434054  54 0.00014 ***
##  3:      7.5      0.5574074     0.7147027 -0.17041673  54 0.00024 ***
##  4:     10.5      0.6314815     0.7227008 -0.08581209  54     0.047 *
##  5:     13.5      0.5351852     0.6812848 -0.14784579  54 0.00032 ***
##  6:     16.5      0.4907407     0.6163897 -0.15815934  54    0.005 **
##  7:     19.5      0.5129630     0.6057373 -0.10594419  54     0.014 *
##  8:     22.5      0.6129630     0.6322030 -0.01267083  54     0.83 :(
##  9:     25.5      0.5537037     0.5850422 -0.03200306  54     0.64 :(
## 10:     28.5      0.5611111     0.5894469 -0.01732044  54     0.63 :(
##     time  error.diff shapes
##  1:  1.5  0.07303014     24
##  2:  4.5 -0.18434054     24
##  3:  7.5 -0.17041673     24
##  4: 10.5 -0.08581209     24
##  5: 13.5 -0.14784579     24
##  6: 16.5 -0.15815934     24
##  7: 19.5 -0.10594419     24
##  8: 22.5 -0.01267083     16
##  9: 25.5 -0.03200306     16
## 10: 28.5 -0.01732044     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74906  -0.19267  -0.05492   0.23540   0.86516  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06264    0.02036   3.076  0.00216 ** 
## timeNorm     0.05239    0.03029   1.729  0.08410 .  
## obj.diff    -0.30669    0.02664 -11.513  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07338495)
## 
##     Null deviance: 74.897  on 890  degrees of freedom
## Residual deviance: 65.166  on 888  degrees of freedom
## AIC: 206.22
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3000000     0.3038003 -0.021976446 162     0.57 :(
##  2:      4.5      0.5086420     0.6435586 -0.118822900  81 1.9e-05 ***
##  3:      7.5      0.4296296     0.5229068 -0.099048300  81   0.0065 **
##  4:     10.5      0.4740741     0.6031879 -0.132293983  81 0.00011 ***
##  5:     13.5      0.4753086     0.5733030 -0.090316862  81   0.0029 **
##  6:     16.5      0.4333333     0.5345376 -0.094416022  81   0.0032 **
##  7:     19.5      0.5222222     0.5914733 -0.055143049  81      0.07 .
##  8:     22.5      0.4283951     0.5257793 -0.094106034  81 0.00089 ***
##  9:     25.5      0.5580247     0.5797558 -0.008270062  81     0.69 :(
## 10:     28.5      0.5160494     0.5619786 -0.059751925  81     0.048 *
##     time   error.diff shapes
##  1:  1.5 -0.021976446     16
##  2:  4.5 -0.118822900     24
##  3:  7.5 -0.099048300     24
##  4: 10.5 -0.132293983     24
##  5: 13.5 -0.090316862     24
##  6: 16.5 -0.094416022     24
##  7: 19.5 -0.055143049     16
##  8: 22.5 -0.094106034     24
##  9: 25.5 -0.008270062     16
## 10: 28.5 -0.059751925     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64976  -0.15267  -0.07143   0.24828   0.73575  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.03381    0.03009   1.124    0.262    
## timeNorm     0.05708    0.04702   1.214    0.226    
## obj.diff    -0.30079    0.04072  -7.387 1.26e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06611709)
## 
##     Null deviance: 25.23  on 329  degrees of freedom
## Residual deviance: 21.62  on 327  degrees of freedom
## AIC: 45.097
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.2250000     0.2398207 -0.02972150 60   0.32 :(
##  2:      4.5      0.4333333     0.5813467 -0.14387812 30 0.0099 **
##  3:      7.5      0.3700000     0.4678668 -0.07002244 30    0.1 :(
##  4:     10.5      0.4133333     0.4639707 -0.06867710 30   0.17 :(
##  5:     13.5      0.3500000     0.4613004 -0.07486892 30   0.036 *
##  6:     16.5      0.3266667     0.4612711 -0.14672095 30 0.0032 **
##  7:     19.5      0.3200000     0.4514902 -0.11134531 30 0.0015 **
##  8:     22.5      0.5000000     0.5050770  0.01525165 30   0.81 :(
##  9:     25.5      0.4800000     0.4813309  0.01240702 30   0.63 :(
## 10:     28.5      0.3933333     0.4606821 -0.04485632 30   0.26 :(
##     time  error.diff shapes
##  1:  1.5 -0.02972150     16
##  2:  4.5 -0.14387812     24
##  3:  7.5 -0.07002244     16
##  4: 10.5 -0.06867710     16
##  5: 13.5 -0.07486892     24
##  6: 16.5 -0.14672095     24
##  7: 19.5 -0.11134531     24
##  8: 22.5  0.01525165     16
##  9: 25.5  0.01240702     16
## 10: 28.5 -0.04485632     16

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71866  -0.13903  -0.07563   0.26274   0.50459  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.42377    0.05076   8.348 1.98e-15 ***
## timeNorm     0.14908    0.04803   3.104  0.00208 ** 
## obj.diff    -0.83399    0.05725 -14.568  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06928841)
## 
##     Null deviance: 39.074  on 329  degrees of freedom
## Residual deviance: 22.657  on 327  degrees of freedom
## AIC: 60.558
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5183333     0.6190299 -0.11343824 60     0.035 *
##  2:      4.5      0.5333333     0.8686553 -0.40875076 30 4.7e-07 ***
##  3:      7.5      0.6433333     0.8280425 -0.21851448 30   0.0026 **
##  4:     10.5      0.6533333     0.7307282 -0.08113246 30     0.21 :(
##  5:     13.5      0.6500000     0.7231805 -0.11138569 30     0.31 :(
##  6:     16.5      0.6800000     0.7634659 -0.08833518 30     0.17 :(
##  7:     19.5      0.6666667     0.6841523 -0.01454153 30     0.89 :(
##  8:     22.5      0.5233333     0.6626856 -0.13957220 30     0.019 *
##  9:     25.5      0.6333333     0.6069621  0.04686400 30     0.67 :(
## 10:     28.5      0.7033333     0.6142242  0.09037720 30     0.18 :(
##     time  error.diff shapes
##  1:  1.5 -0.11343824     24
##  2:  4.5 -0.40875076     24
##  3:  7.5 -0.21851448     24
##  4: 10.5 -0.08113246     16
##  5: 13.5 -0.11138569     16
##  6: 16.5 -0.08833518     16
##  7: 19.5 -0.01454153     16
##  8: 22.5 -0.13957220     24
##  9: 25.5  0.04686400     16
## 10: 28.5  0.09037720     16
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61859  -0.10903  -0.00671   0.07862   0.58880  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.36691    0.02494  14.710   <2e-16 ***
## timeNorm     0.04858    0.03107   1.563    0.118    
## obj.diff    -0.74625    0.03169 -23.552   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0527652)
## 
##     Null deviance: 61.012  on 593  degrees of freedom
## Residual deviance: 31.184  on 591  degrees of freedom
## AIC: -56.799
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4250000     0.3818097  0.04228909 108     0.095 .
##  2:      4.5      0.5703704     0.7600559 -0.20253863  54 3.2e-05 ***
##  3:      7.5      0.5555556     0.6218983 -0.09003742  54     0.046 *
##  4:     10.5      0.6055556     0.5834307  0.00810360  54     0.84 :(
##  5:     13.5      0.5592593     0.5950296 -0.04254806  54     0.29 :(
##  6:     16.5      0.5703704     0.6040416 -0.04104681  54     0.27 :(
##  7:     19.5      0.5074074     0.5492649 -0.04685252  54     0.26 :(
##  8:     22.5      0.5481481     0.4793441  0.07132819  54     0.15 :(
##  9:     25.5      0.5314815     0.4559805  0.08431132  54     0.073 .
## 10:     28.5      0.4648148     0.3909264  0.07818103  54     0.17 :(
##     time  error.diff shapes
##  1:  1.5  0.04228909     16
##  2:  4.5 -0.20253863     24
##  3:  7.5 -0.09003742     24
##  4: 10.5  0.00810360     16
##  5: 13.5 -0.04254806     16
##  6: 16.5 -0.04104681     16
##  7: 19.5 -0.04685252     16
##  8: 22.5  0.07132819     16
##  9: 25.5  0.08431132     16
## 10: 28.5  0.07818103     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64341  -0.23071  -0.01188   0.22146   0.76386  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.19659    0.01984   9.911   <2e-16 ***
## timeNorm     0.01537    0.02837   0.542    0.588    
## obj.diff    -0.46502    0.03048 -15.257   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07026932)
## 
##     Null deviance: 83.775  on 956  degrees of freedom
## Residual deviance: 67.037  on 954  degrees of freedom
## AIC: 179.61
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.3316092     0.2404667  0.10902062 174 0.0013 **
##  2:      4.5      0.4609195     0.5088613 -0.05709143  87   0.12 :(
##  3:      7.5      0.4344828     0.4454615 -0.01096261  87   0.77 :(
##  4:     10.5      0.4218391     0.4450093 -0.02077395  87   0.54 :(
##  5:     13.5      0.4425287     0.4084377  0.04437001  87   0.25 :(
##  6:     16.5      0.4126437     0.3529078  0.05198053  87   0.13 :(
##  7:     19.5      0.3620690     0.2909555  0.06629129  87   0.052 .
##  8:     22.5      0.3206897     0.2755358  0.02510627  87   0.36 :(
##  9:     25.5      0.3586207     0.2656083  0.09568731  87 0.0079 **
## 10:     28.5      0.3505747     0.2360832  0.10488729  87 0.0058 **
##     time  error.diff shapes
##  1:  1.5  0.10902062     24
##  2:  4.5 -0.05709143     16
##  3:  7.5 -0.01096261     16
##  4: 10.5 -0.02077395     16
##  5: 13.5  0.04437001     16
##  6: 16.5  0.05198053     16
##  7: 19.5  0.06629129     16
##  8: 22.5  0.02510627     16
##  9: 25.5  0.09568731     24
## 10: 28.5  0.10488729     24

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #